Downloads & Free Reading Options - Results
Regression Modeling by Michael J. Panik
Read "Regression Modeling" by Michael J. Panik through these free online access and download options.
Books Results
Source: The Internet Archive
The internet Archive Search Results
Available books for downloads and borrow from The internet Archive
1Modeling And Analyzing The Accidents Severity Due To The Lack Of Attention To The Front In Corona Pandemic Restrictions Using Logistic Regression
By maziar abolfazlzadeh; seyed ebrahim abdolmanafi; hassan khaksar
Traffic accidents are one of the main causes of death in the world and the resulting damages have an important effect on the economy of any country that the need to identify factors affecting accidents that lead to a reduction in the frequency or severity of accidents is understandable. Since the outbreak of Covid-19, travel restriction policies have been widely adopted by cities around the world that it played a profound role in changing the shape of urban travel patterns. One of the factors that influenced traffic behavior in recent years was the epidemic of Corona disease in the world. Therefore, new driving rules and regulations were established for urban and suburban traffic, and traffic behaviors were affected by those rules. It is possible to investigate the effect of each factor on the severity of accidents by using accident severity models according to the effective parameters. In this article, urban accidents due to lack of attention during the corona epidemic were analyzed using logistic regression and information related to intra-urban accidents in Rasht city. The final model shows that the independent variables of working day, at-fault male drivers over 60 years of age, front-to-front collision and the at-fault vehicle Pride increase the probability of a fatal or injury accident compared to a damage accident. Variables with negative coefficients (6:00 a.m. to 9:00 p.m., dry road surface conditions, clear weather, and daylight conditions) reduce the probability of fatal or injury accidents compared to damage accidents.
“Modeling And Analyzing The Accidents Severity Due To The Lack Of Attention To The Front In Corona Pandemic Restrictions Using Logistic Regression” Metadata:
- Title: ➤ Modeling And Analyzing The Accidents Severity Due To The Lack Of Attention To The Front In Corona Pandemic Restrictions Using Logistic Regression
- Author: ➤ maziar abolfazlzadeh; seyed ebrahim abdolmanafi; hassan khaksar
- Language: per
Edition Identifiers:
- Internet Archive ID: ➤ httpscivil-ferdowsi.um.ac.irarticle_43991.htmllangen
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 7.13 Mbs, the file-s for this book were downloaded 11 times, the file-s went public at Sat Mar 23 2024.
Available formats:
Archive BitTorrent - DjVuTXT - Djvu XML - Item Tile - Metadata - OCR Page Index - OCR Search Text - Page Numbers JSON - Scandata - Single Page Processed JP2 ZIP - Text PDF - chOCR - hOCR -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Modeling And Analyzing The Accidents Severity Due To The Lack Of Attention To The Front In Corona Pandemic Restrictions Using Logistic Regression at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
2Modeling Inequality And Spread In Multiple Regression
By Rolf Aaberge, Steinar Bjerve and Kjell Doksum
We consider concepts and models for measuring inequality in the distribution of resources with a focus on how inequality varies as a function of covariates. Lorenz introduced a device for measuring inequality in the distribution of income that indicates how much the incomes below the u$^{th}$ quantile fall short of the egalitarian situation where everyone has the same income. Gini introduced a summary measure of inequality that is the average over u of the difference between the Lorenz curve and its values in the egalitarian case. More generally, measures of inequality are useful for other response variables in addition to income, e.g. wealth, sales, dividends, taxes, market share and test scores. In this paper we show that a generalized van Zwet type dispersion ordering for distributions of positive random variables induces an ordering on the Lorenz curve, the Gini coefficient and other measures of inequality. We use this result and distributional orderings based on transformations of distributions to motivate parametric and semiparametric models whose regression coefficients measure effects of covariates on inequality. In particular, we extend a parametric Pareto regression model to a flexible semiparametric regression model and give partial likelihood estimates of the regression coefficients and a baseline distribution that can be used to construct estimates of the various conditional measures of inequality.
“Modeling Inequality And Spread In Multiple Regression” Metadata:
- Title: ➤ Modeling Inequality And Spread In Multiple Regression
- Authors: Rolf AabergeSteinar BjerveKjell Doksum
- Language: English
Edition Identifiers:
- Internet Archive ID: arxiv-math0610852
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 6.33 Mbs, the file-s for this book were downloaded 86 times, the file-s went public at Thu Sep 19 2013.
Available formats:
Abbyy GZ - Animated GIF - Archive BitTorrent - DjVu - DjVuTXT - Djvu XML - Item Tile - Metadata - Scandata - Single Page Processed JP2 ZIP - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Modeling Inequality And Spread In Multiple Regression at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
3Modeling For Dynamic Ordinal Regression Relationships: An Application To Estimating Maturity Of Rockfish In California
By Maria DeYoreo and Athanasios Kottas
We develop a Bayesian nonparametric framework for modeling ordinal regression relationships which evolve in discrete time. The motivating application involves a key problem in fisheries research on estimating dynamically evolving relationships between age, length and maturity, the latter recorded on an ordinal scale. The methodology builds from nonparametric mixture modeling for the joint stochastic mechanism of covariates and latent continuous responses. This approach yields highly flexible inference for ordinal regression functions while at the same time avoiding the computational challenges of parametric models. A novel dependent Dirichlet process prior for time-dependent mixing distributions extends the model to the dynamic setting. The methodology is used for a detailed study of relationships between maturity, age, and length for Chilipepper rockfish, using data collected over 15 years along the coast of California.
“Modeling For Dynamic Ordinal Regression Relationships: An Application To Estimating Maturity Of Rockfish In California” Metadata:
- Title: ➤ Modeling For Dynamic Ordinal Regression Relationships: An Application To Estimating Maturity Of Rockfish In California
- Authors: Maria DeYoreoAthanasios Kottas
- Language: English
“Modeling For Dynamic Ordinal Regression Relationships: An Application To Estimating Maturity Of Rockfish In California” Subjects and Themes:
- Subjects: Statistics - Applications
Edition Identifiers:
- Internet Archive ID: arxiv-1507.01242
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 20.54 Mbs, the file-s for this book were downloaded 32 times, the file-s went public at Thu Jun 28 2018.
Available formats:
Abbyy GZ - Archive BitTorrent - DjVuTXT - Djvu XML - JPEG Thumb - Metadata - Scandata - Single Page Processed JP2 ZIP - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Modeling For Dynamic Ordinal Regression Relationships: An Application To Estimating Maturity Of Rockfish In California at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
4Modeling F/A-18 Flight Hour Program Costs Using Regression Analysis
By Arkley, Larry E.
We develop a Bayesian nonparametric framework for modeling ordinal regression relationships which evolve in discrete time. The motivating application involves a key problem in fisheries research on estimating dynamically evolving relationships between age, length and maturity, the latter recorded on an ordinal scale. The methodology builds from nonparametric mixture modeling for the joint stochastic mechanism of covariates and latent continuous responses. This approach yields highly flexible inference for ordinal regression functions while at the same time avoiding the computational challenges of parametric models. A novel dependent Dirichlet process prior for time-dependent mixing distributions extends the model to the dynamic setting. The methodology is used for a detailed study of relationships between maturity, age, and length for Chilipepper rockfish, using data collected over 15 years along the coast of California.
“Modeling F/A-18 Flight Hour Program Costs Using Regression Analysis” Metadata:
- Title: ➤ Modeling F/A-18 Flight Hour Program Costs Using Regression Analysis
- Author: Arkley, Larry E.
- Language: en_US
Edition Identifiers:
- Internet Archive ID: modelingfa18flig00arkl
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 181.16 Mbs, the file-s for this book were downloaded 323 times, the file-s went public at Thu Jan 24 2013.
Available formats:
Abbyy GZ - Animated GIF - Archive BitTorrent - Cloth Cover Detection Log - Contents - DjVu - DjVuTXT - Djvu XML - Dublin Core - Item Tile - MARC - MARC Binary - MARC Source - Metadata - OCR Page Index - OCR Search Text - Page Numbers JSON - Scandata - Single Page Original JP2 Tar - Single Page Processed JP2 ZIP - Text PDF - chOCR - hOCR -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Modeling F/A-18 Flight Hour Program Costs Using Regression Analysis at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
5Introduction To Linear Regression – Predictive Modeling Made Easy
Welcome to Imarticus Learning! In this video, we delve into one of the most fundamental and widely used algorithms in Machine Learning and Data Science — Linear Regression . Whether you're a beginner eager to understand the basics or a professional looking to reinforce your skills, this session covers both Simple and Multiple Linear Regression in a clear, concise manner. You’ll explore key concepts such as the linear regression equation, model evaluation metrics like R² Score, MAE, MSE, and RMSE, along with core assumptions like linearity, independence, multicollinearity, and homoscedasticity. This is an essential starting point for anyone pursuing the best machine learning course to build a career in predictive analytics. At Imarticus Learning, we make complex topics easy to understand with expert-led instruction, flexible learning formats, and robust support systems including mock tests, study materials, and mentorship. If you're looking to upskill with industry-relevant tools and concepts, this is the perfect video to get started.
“Introduction To Linear Regression – Predictive Modeling Made Easy” Metadata:
- Title: ➤ Introduction To Linear Regression – Predictive Modeling Made Easy
“Introduction To Linear Regression – Predictive Modeling Made Easy” Subjects and Themes:
Edition Identifiers:
- Internet Archive ID: videoplayback_20250430
Downloads Information:
The book is available for download in "movies" format, the size of the file-s is: 98.31 Mbs, the file-s for this book were downloaded 3 times, the file-s went public at Wed Apr 30 2025.
Available formats:
Archive BitTorrent - Item Tile - Metadata - Thumbnail - WebM - h.264 -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Introduction To Linear Regression – Predictive Modeling Made Easy at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
6Modeling Rheological Properties Of Oil Well Cement Slurries Using Multiple Regression Analysis And Artificial Neural Networks
Artificial neural networks (ANN) and multiple regression analysis (MRA) were used to predict the rheological properties of oil well cement slurries. The slurries were prepared using class G oil well cement with a water-cement mass ratio (w/c) of 0.44, and incorporating a new generation polycarboxylate-based high-range water reducing admixture (PCH), polycarboxlate-based mid-range water reducing admixture (PCM), and lignosulphonate-based mid-range water reducing admixture (LSM). The rheological properties were investigated at different temperatures in the range of 23 to 60ºC using an advanced shear-stress/shear-strain controlled rheometer. Experimental data thus obtained were used to develop predictive models based on back-propagation artificial neural networks and multiple regression analysis. It was found that both ANN and MRA depicted good agreement with the experimental data, with ANN achieving more accurate predictions. The developed models could effectively predict the rheological properties of new slurries designed within the range of input parameters of the experimental database with an absolute error of 3.43, 3.17, and 2.82%, in the case of ANN and 4.83, 6.32, and 5.05%, in the case of MRA, for slurries incorporating PCH, PCM, and LSM, respectively. The flow curves developed using ANN and MRA allowed predicting the Bingham parameters (yield stress and plastic viscosity) of the oil well slurries with adequate accuracy.
“Modeling Rheological Properties Of Oil Well Cement Slurries Using Multiple Regression Analysis And Artificial Neural Networks” Metadata:
- Title: ➤ Modeling Rheological Properties Of Oil Well Cement Slurries Using Multiple Regression Analysis And Artificial Neural Networks
- Language: English
“Modeling Rheological Properties Of Oil Well Cement Slurries Using Multiple Regression Analysis And Artificial Neural Networks” Subjects and Themes:
- Subjects: ➤ Cement slurry - Oil well - Yield stress - Plastic viscosity - Artificial neural network - Multiple regression analysis.
Edition Identifiers:
- Internet Archive ID: IJMS10120
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 10.03 Mbs, the file-s for this book were downloaded 282 times, the file-s went public at Sun Feb 09 2014.
Available formats:
Abbyy GZ - Animated GIF - Archive BitTorrent - DjVu - DjVuTXT - Djvu XML - JPEG Thumb - Metadata - Scandata - Single Page Processed JP2 ZIP - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Modeling Rheological Properties Of Oil Well Cement Slurries Using Multiple Regression Analysis And Artificial Neural Networks at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
7Modeling And Interpreting Interactive Hypotheses In Regression Analysis
By Kam, Cindy D., 1975-
Artificial neural networks (ANN) and multiple regression analysis (MRA) were used to predict the rheological properties of oil well cement slurries. The slurries were prepared using class G oil well cement with a water-cement mass ratio (w/c) of 0.44, and incorporating a new generation polycarboxylate-based high-range water reducing admixture (PCH), polycarboxlate-based mid-range water reducing admixture (PCM), and lignosulphonate-based mid-range water reducing admixture (LSM). The rheological properties were investigated at different temperatures in the range of 23 to 60ºC using an advanced shear-stress/shear-strain controlled rheometer. Experimental data thus obtained were used to develop predictive models based on back-propagation artificial neural networks and multiple regression analysis. It was found that both ANN and MRA depicted good agreement with the experimental data, with ANN achieving more accurate predictions. The developed models could effectively predict the rheological properties of new slurries designed within the range of input parameters of the experimental database with an absolute error of 3.43, 3.17, and 2.82%, in the case of ANN and 4.83, 6.32, and 5.05%, in the case of MRA, for slurries incorporating PCH, PCM, and LSM, respectively. The flow curves developed using ANN and MRA allowed predicting the Bingham parameters (yield stress and plastic viscosity) of the oil well slurries with adequate accuracy.
“Modeling And Interpreting Interactive Hypotheses In Regression Analysis” Metadata:
- Title: ➤ Modeling And Interpreting Interactive Hypotheses In Regression Analysis
- Author: Kam, Cindy D., 1975-
- Language: English
“Modeling And Interpreting Interactive Hypotheses In Regression Analysis” Subjects and Themes:
- Subjects: ➤ Regression analysis - Social sciences -- Statistical methods
Edition Identifiers:
- Internet Archive ID: modelinginterpre0000kamc
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 576.41 Mbs, the file-s for this book were downloaded 19 times, the file-s went public at Sun Oct 02 2022.
Available formats:
ACS Encrypted PDF - AVIF Thumbnails ZIP - Cloth Cover Detection Log - DjVuTXT - Djvu XML - Dublin Core - EPUB - Item Tile - JPEG Thumb - JSON - LCP Encrypted EPUB - LCP Encrypted PDF - Log - MARC - MARC Binary - Metadata - OCR Page Index - OCR Search Text - PNG - Page Numbers JSON - RePublisher Final Processing Log - RePublisher Initial Processing Log - Scandata - Single Page Original JP2 Tar - Single Page Processed JP2 ZIP - Text PDF - Title Page Detection Log - chOCR - hOCR -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Modeling And Interpreting Interactive Hypotheses In Regression Analysis at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
8DTIC ADA267132: A Comparison Of Neural Network And Regression Models For Navy Retention Modeling
By Defense Technical Information Center
This thesis evaluates a possible use of artificial neural networks for military manpower and personnel analysis. Two neural network models were constructed to predict the reenlistment behavior of a select group of individuals in the Navy, from a sample of 680 individuals. The data were extracted from the 1985 DoD Survey of Officer and Enlisted Personnel. Explanatory variables were grouped into demographic/personal, military characteristics, perceived probability of civilian employment, educational level, and satisfaction with military life and military benefits. The first neural network model was compared to a more traditional method of statistical modeling (logistic regression analysis) to determine the strengths and weaknesses of the neural network model. Both models used the same set of 17 variables and were tested using a holdout sample of 100 observations. The neural network model was found to be comparable to the logistic regression model as a predictor, but deficient as a policy analysis model. The second neural network model was constructed using the same data set and architecture as the first neural network model, including the original 17 variables, plus an additional II variables that consisted of variables with and without theoretical foundation for predicting reenlistment. The two neural network models were then compared and found to be similar at predicting reenlistment. Both neural network models were considered to be deficient as tools for policy analysts.... Artificial neural networks, Neural networks, Reenlistment behavior.
“DTIC ADA267132: A Comparison Of Neural Network And Regression Models For Navy Retention Modeling” Metadata:
- Title: ➤ DTIC ADA267132: A Comparison Of Neural Network And Regression Models For Navy Retention Modeling
- Author: ➤ Defense Technical Information Center
- Language: English
“DTIC ADA267132: A Comparison Of Neural Network And Regression Models For Navy Retention Modeling” Subjects and Themes:
- Subjects: ➤ DTIC Archive - Russell, Bradley S - NAVAL POSTGRADUATE SCHOOL MONTEREY CA - *NEURAL NETS - *REGRESSION ANALYSIS - *COMPUTER NETWORKS - *PERSONNEL RETENTION - MATHEMATICAL MODELS - POLICIES - EMPLOYMENT - NAVAL PERSONNEL - COMPARISON - SURVEYS - BEHAVIOR - ANALYSTS - REENLISTMENT - BENEFITS - ARCHITECTURE - OFFICER PERSONNEL - LOGISTICS - MANPOWER - OBSERVATION - PROBABILITY - ENLISTED PERSONNEL - THESES - VARIABLES
Edition Identifiers:
- Internet Archive ID: DTIC_ADA267132
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 64.56 Mbs, the file-s for this book were downloaded 57 times, the file-s went public at Sun Mar 11 2018.
Available formats:
Abbyy GZ - Archive BitTorrent - DjVuTXT - Djvu XML - Item Tile - Metadata - OCR Page Index - OCR Search Text - Page Numbers JSON - Scandata - Single Page Processed JP2 ZIP - Text PDF - chOCR - hOCR -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find DTIC ADA267132: A Comparison Of Neural Network And Regression Models For Navy Retention Modeling at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
9Modeling Choice Considerations And Frontier Efficiency Estimates: Takeaways From A Systematic Literature Review And Meta-Regression Analysis
By AlJawhara AlSabah
Meta-regression analysis of reviewed studies on hospital efficiency using non-parametric frontier-based estimation methodology only; any form of Data Envelopment Analysis (DEA) or its extensions to evaluate technical efficiency, pure technical efficiency, or scale efficiency of DMUs.
“Modeling Choice Considerations And Frontier Efficiency Estimates: Takeaways From A Systematic Literature Review And Meta-Regression Analysis” Metadata:
- Title: ➤ Modeling Choice Considerations And Frontier Efficiency Estimates: Takeaways From A Systematic Literature Review And Meta-Regression Analysis
- Author: AlJawhara AlSabah
Edition Identifiers:
- Internet Archive ID: osf-registrations-k8t2f-v1
Downloads Information:
The book is available for download in "data" format, the size of the file-s is: 0.10 Mbs, the file-s for this book were downloaded 4 times, the file-s went public at Sat Apr 30 2022.
Available formats:
Archive BitTorrent - Metadata - ZIP -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Modeling Choice Considerations And Frontier Efficiency Estimates: Takeaways From A Systematic Literature Review And Meta-Regression Analysis at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
10Modeling And Zoning Of Fire Prone Areas In Zagros Forests Using Geographic Information System Based On Logistic Regression
By The Journal of Geography and Environmental Hazards
Forests play a vital role in the sustainability of ecosystems as one of the most important natural renewable resources. One of the most important disturbances affecting Zagros forest ecosystems is forest fires. Therefore, identifying critical fire areas to reduce potential damage is necessary. The purpose of this study is to investigate the effect of effective variables in causing fire and to prepare a fire risk map. For this purpose, the variables affecting the fire including altitude, slope, direction, Distance from residential areas, distance from waterways and distance from the road were determined to determine the impact of each on the fire. Elevation map of sea level, slope and geographical direction was prepared with the help of digital elevation model. Distance maps of residential areas and distance from the road were prepared from digital maps of 1.25000. Also, the areas where fires occurred during the years 90-94 were harvested by GPS. In this study, logistic regression method was used to investigate the effect of various factors on fire. The results showed that altitude, distance from waterway and slope percentage were the most important factors influencing forest fires in the region. Modeling was performed based on three variables that had a significant relationship with forest fires in the region and the coefficients obtained from the logistic regression method. The validation results of the model with a Nagelkerke's R 2 coefficient of about 0.500 and a rock curve coefficient of 0.701 showed the accuracy, fit and validity of the obtained model. The results also showed that 81% of the area is located in critical and dangerous areas.
“Modeling And Zoning Of Fire Prone Areas In Zagros Forests Using Geographic Information System Based On Logistic Regression” Metadata:
- Title: ➤ Modeling And Zoning Of Fire Prone Areas In Zagros Forests Using Geographic Information System Based On Logistic Regression
- Author: ➤ The Journal of Geography and Environmental Hazards
- Language: per
“Modeling And Zoning Of Fire Prone Areas In Zagros Forests Using Geographic Information System Based On Logistic Regression” Subjects and Themes:
- Subjects: Fire - Rock - Logistic Regression - Modeling
Edition Identifiers:
- Internet Archive ID: ➤ geoeh-volume-10-issue-2-pages-43-58
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 7.42 Mbs, the file-s for this book were downloaded 8 times, the file-s went public at Mon Dec 09 2024.
Available formats:
Archive BitTorrent - DjVuTXT - Djvu XML - Item Tile - Metadata - OCR Page Index - OCR Search Text - Page Numbers JSON - Scandata - Single Page Processed JP2 ZIP - Text PDF - chOCR - hOCR -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Modeling And Zoning Of Fire Prone Areas In Zagros Forests Using Geographic Information System Based On Logistic Regression at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
11Comment: Struggles With Survey Weighting And Regression Modeling
By F. Jay Breidt and Jean D. Opsomer
Comment: Struggles with Survey Weighting and Regression Modeling [arXiv:0710.5005]
“Comment: Struggles With Survey Weighting And Regression Modeling” Metadata:
- Title: ➤ Comment: Struggles With Survey Weighting And Regression Modeling
- Authors: F. Jay BreidtJean D. Opsomer
- Language: English
Edition Identifiers:
- Internet Archive ID: arxiv-0710.5012
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 2.19 Mbs, the file-s for this book were downloaded 273 times, the file-s went public at Mon Sep 23 2013.
Available formats:
Abbyy GZ - Animated GIF - Archive BitTorrent - DjVu - DjVuTXT - Djvu XML - Item Tile - Metadata - Scandata - Single Page Processed JP2 ZIP - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Comment: Struggles With Survey Weighting And Regression Modeling at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
12Nonlinear Regression Modeling : A Unified Practical Approach
By Ratkowsky, David A., 1935-
Comment: Struggles with Survey Weighting and Regression Modeling [arXiv:0710.5005]
“Nonlinear Regression Modeling : A Unified Practical Approach” Metadata:
- Title: ➤ Nonlinear Regression Modeling : A Unified Practical Approach
- Author: Ratkowsky, David A., 1935-
- Language: English
“Nonlinear Regression Modeling : A Unified Practical Approach” Subjects and Themes:
- Subjects: Regression analysis - Parameter estimation
Edition Identifiers:
- Internet Archive ID: nonlinearregress0000ratk
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 662.49 Mbs, the file-s for this book were downloaded 217 times, the file-s went public at Fri Jan 21 2022.
Available formats:
ACS Encrypted PDF - Cloth Cover Detection Log - DjVuTXT - Djvu XML - Dublin Core - EPUB - Item Tile - JPEG Thumb - JSON - LCP Encrypted EPUB - LCP Encrypted PDF - Log - MARC - MARC Binary - Metadata - OCR Page Index - OCR Search Text - PNG - Page Numbers JSON - Scandata - Single Page Original JP2 Tar - Single Page Processed JP2 ZIP - Text PDF - Title Page Detection Log - chOCR - hOCR -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Nonlinear Regression Modeling : A Unified Practical Approach at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
13Neutrosophic Regression Modeling With Dummy Variables: Applications And Simulations
By Muhammad Aslam, Osama H. Arif
In this paper, we introduce a regression model using dummy variables within the framework of neutrosophic statistics. This model is designed for regression analysis under conditions of uncertainty, extending the classical regression model with dummy variables. We also present regression and analysis of variance under neutrosophic statistics. The application of our model is demonstrated through simulation and comparative studies, showing that the results differ from those obtained using classical regression. Our findings indicate that the degree of uncertainty significantly impacts the predicted and residual values.
“Neutrosophic Regression Modeling With Dummy Variables: Applications And Simulations” Metadata:
- Title: ➤ Neutrosophic Regression Modeling With Dummy Variables: Applications And Simulations
- Author: Muhammad Aslam, Osama H. Arif
- Language: English
“Neutrosophic Regression Modeling With Dummy Variables: Applications And Simulations” Subjects and Themes:
- Subjects: regression analysis - neutrosophic statistics
Edition Identifiers:
- Internet Archive ID: ➤ neutrosophic-regression-_ijaa3294
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 7.47 Mbs, the file-s for this book were downloaded 3 times, the file-s went public at Mon Nov 11 2024.
Available formats:
Archive BitTorrent - DjVuTXT - Djvu XML - Metadata - OCR Page Index - OCR Search Text - Page Numbers JSON - Scandata - Single Page Processed JP2 ZIP - Text PDF - chOCR - hOCR -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Neutrosophic Regression Modeling With Dummy Variables: Applications And Simulations at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
14Threshold Regression For Survival Analysis: Modeling Event Times By A Stochastic Process Reaching A Boundary
By Mei-Ling Ting Lee and G. A. Whitmore
Many researchers have investigated first hitting times as models for survival data. First hitting times arise naturally in many types of stochastic processes, ranging from Wiener processes to Markov chains. In a survival context, the state of the underlying process represents the strength of an item or the health of an individual. The item fails or the individual experiences a clinical endpoint when the process reaches an adverse threshold state for the first time. The time scale can be calendar time or some other operational measure of degradation or disease progression. In many applications, the process is latent (i.e., unobservable). Threshold regression refers to first-hitting-time models with regression structures that accommodate covariate data. The parameters of the process, threshold state and time scale may depend on the covariates. This paper reviews aspects of this topic and discusses fruitful avenues for future research.
“Threshold Regression For Survival Analysis: Modeling Event Times By A Stochastic Process Reaching A Boundary” Metadata:
- Title: ➤ Threshold Regression For Survival Analysis: Modeling Event Times By A Stochastic Process Reaching A Boundary
- Authors: Mei-Ling Ting LeeG. A. Whitmore
- Language: English
Edition Identifiers:
- Internet Archive ID: arxiv-0708.0346
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 12.39 Mbs, the file-s for this book were downloaded 255 times, the file-s went public at Thu Sep 19 2013.
Available formats:
Abbyy GZ - Animated GIF - Archive BitTorrent - DjVu - DjVuTXT - Djvu XML - Item Tile - Metadata - Scandata - Single Page Processed JP2 ZIP - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Threshold Regression For Survival Analysis: Modeling Event Times By A Stochastic Process Reaching A Boundary at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
15ERIC ED435678: Three-Dimensional Modeling In Linear Regression.
By ERIC
Linear regression examines the relationship between one or more independent (predictor) variables and a dependent variable. By using a particular formula, regression determines the weights needed to minimize the error term for a given set of predictors. With one predictor variable, the relationship between the predictor and the dependent variable is linear. With two predictors, this relationship becomes planar, and with three or more predictors, this relationship becomes hyper planar. By examining three-dimensional representations of the data, a researcher can gain greater insight into the data. The recent report of the American Psychological Association Task Force on Statistical Inference, published in the August 1999 issue of the "American Psychologist," emphasizes the value and importance of using graphics to understand and communicate data dynamics. (Contains 11 figures, 7 tables, and 13 references.) (SLD)
“ERIC ED435678: Three-Dimensional Modeling In Linear Regression.” Metadata:
- Title: ➤ ERIC ED435678: Three-Dimensional Modeling In Linear Regression.
- Author: ERIC
- Language: English
“ERIC ED435678: Three-Dimensional Modeling In Linear Regression.” Subjects and Themes:
- Subjects: ERIC Archive - Predictor Variables - Regression (Statistics) - Three Dimensional Aids - Herman, James D.
Edition Identifiers:
- Internet Archive ID: ERIC_ED435678
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 21.63 Mbs, the file-s for this book were downloaded 83 times, the file-s went public at Thu Dec 31 2015.
Available formats:
Abbyy GZ - Animated GIF - Archive BitTorrent - DjVu - DjVuTXT - Djvu XML - Item Tile - Metadata - Scandata - Single Page Processed JP2 ZIP - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find ERIC ED435678: Three-Dimensional Modeling In Linear Regression. at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
16An Exploratory Study Of Pre-admission Predictors Of Hardiness And Retention For United States Military Academy Cadets Using Regression Modeling
By Comeaux, Aris J.
This study uses regression techniques on United States Military Academy (USMA) cadet/ candidate data in order to develop a hardiness-prediction model and explore retention during and after graduation from USMA. We created several data sets using 42 variables from three cohorts (N= 3,716) and analyzed them using regression techniques. Preliminary results showed high school type and the interaction between gender and parents education level as significant. Specifically, private religious high schools and male cadets with less-educated fathers are positive predictors of hardiness (R2 = 0.05). Model quality improved in subsequent regressions by identifying a target population. Among varsity football players (N= 149), less-educated mothers and liberal political views are negative predictors of hardiness while race and parents military service history (African Americans with fathers who served in the military) and prep school attendance are positive predictors of hardiness (R2 = 0.97). Logistic regression results suggest military, physical, and academic performance are positive predictors of USMA retention while hardiness-challenge, participation in varsity athletics, and less-educated fathers are negative predictors. Logistic regression results identified basic branch as the sole positive predictor of U.S. Army officer retention beyond a USMA graduates sixth year of active federal service. Infantry officers, followed by military police, armor and engineers, remain in service longer (medical corps and aviation branch officers excluded).
“An Exploratory Study Of Pre-admission Predictors Of Hardiness And Retention For United States Military Academy Cadets Using Regression Modeling” Metadata:
- Title: ➤ An Exploratory Study Of Pre-admission Predictors Of Hardiness And Retention For United States Military Academy Cadets Using Regression Modeling
- Author: Comeaux, Aris J.
- Language: English
“An Exploratory Study Of Pre-admission Predictors Of Hardiness And Retention For United States Military Academy Cadets Using Regression Modeling” Subjects and Themes:
- Subjects: ➤ Hardiness - retention - United States Military Academy - West Point cadets - service academy admissions - predictors of performance
Edition Identifiers:
- Internet Archive ID: anexploratorystu1094534647
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 102.78 Mbs, the file-s for this book were downloaded 20 times, the file-s went public at Sun May 05 2019.
Available formats:
Abbyy GZ - Archive BitTorrent - DjVuTXT - Djvu XML - Item Tile - Metadata - Scandata - Single Page Processed JP2 ZIP - Text PDF - Unknown -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find An Exploratory Study Of Pre-admission Predictors Of Hardiness And Retention For United States Military Academy Cadets Using Regression Modeling at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
17Rejoinder: Struggles With Survey Weighting And Regression Modeling
By Andrew Gelman
Rejoinder: Struggles with Survey Weighting and Regression Modeling [arXiv:0710.5005]
“Rejoinder: Struggles With Survey Weighting And Regression Modeling” Metadata:
- Title: ➤ Rejoinder: Struggles With Survey Weighting And Regression Modeling
- Author: Andrew Gelman
- Language: English
Edition Identifiers:
- Internet Archive ID: arxiv-0710.5019
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 4.01 Mbs, the file-s for this book were downloaded 68 times, the file-s went public at Mon Sep 23 2013.
Available formats:
Abbyy GZ - Animated GIF - Archive BitTorrent - DjVu - DjVuTXT - Djvu XML - Item Tile - Metadata - Scandata - Single Page Processed JP2 ZIP - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Rejoinder: Struggles With Survey Weighting And Regression Modeling at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
18Modeling The Relationship Between Teleconnection Indexes With Warm Season Temperature Anomalies In Iran Using Multivariate Regression
By The Journal of Geography and Environmental Hazards
. Introduction Climatic variation is one of the inherent features of the climate system. The components of the climate system are diverse and complex, so that these components interact with each other in a Interweaving way, so that the change in each component eventually changes other components as well. The climate indicators are defined to describe the status of the climate system and its changes. Each climatic index describes some aspects of the climate based on certain parameters. Therefore, various climate indicators have been proposed and used in many studies. Climatic indices are measurable and computable and correlate with some of the elements of the climate in different regions. Some atmospheric variables such as pressure, temperature, precipitation and radiation, as well as non-atmospheric parameters such as sea surface temperature (SST) or ice cover, are among the factors to be considered for climate forcing in different parts of the world. The large water resources, such as seas and oceans, are among the most important climatic operators. These resources are capable of storing a large part of the solar energy and also, due to their fluid nature, are capable of transporting energy to other parts of the planet in various ways (surface flow, subsurface flow, convection, and moisture advection). Changes in ocean behavior, therefore, cause changes in atmospheric patterns, which can further change the short and long-term climatic conditions in different regions. For this reason, ocean surface temperature can be considered as one of the important indicators affecting climatic abnormalities. All patterns of teleconnection as natural phenomena's, are resulting from the turbulent nature of the atmosphere and its internal energy resources. These patterns represent macro-scale variations in atmospheric wave patterns and jetstream flows, and affect the distribution of temperature, precipitation, storm paths, and the status and pattern and speed of the jetstream in large areas. For this reason, the patterns of teleconnection lead to abnormalities that occur simultaneously in very distant areas (Asakere, 2007; 48). In fact, the variability of the behavior of the atmosphere is a result of the set of behaviors and interactions between the ocean and the atmosphere. Hence, indicators that explain the abnormal behaviors of the ocean and therefore the atmosphere can help to identify the causes and nature of the occurrence of short and long-term climate abnormalities in a region. The study of air temperature anomalies in the warm season in Iran in relation to the most important oceanographic and atmospheric indices is the main aim of this research. 2. Material and Methods In this study, two different databases were used including the data of the IRIMO stations and indexes data of oceanographic and atmospheric teleconnection of the NOAA Data Center, affiliated to the U.S. Center for Oceanography Studies. The data of the IRIMO stations consist of 30 synoptic stations with a period of 50 years of data (1961-2010). In the first step, the standardized temperature of each station was calculated per each month during the warm period of the year (from May to September). Then, for the purpose of detecting anomalies, a function was defined in Excel macro as; -0.5 >x> +0.5, and from among the 250 months examined the anomalies (at least 20 stations from 30 stations), 57 cases with anomalies among whole months were selected in the study period, and then by the Pearson correlation method, a relation was calculated between the 17 selected atmospheric-oceanic indicators and the air temperature. The indicators used in this study are the most important indicators introduced in international studies. Also, by using multivariate regression, optimal parameters and regression functions are presented in order to explain and predict the relationship between indices and temperature anomalies in the warm season in the whole of Iran. 3. Results and Discussion The air temperature of Iran shows a significant relationship with the teleconnection indexes. According to the tests performed in selective stations, in general, NINO3, NINO1+2, NINO3.4, NINO4, GBI, CAR, PACEFIC WARM POOL and GLOBAL MEAN TEMP indexes were have a significant correlation in 90% confidence level. In terms of time in calculations with monthly synchronous steps at selected stations, the best indexes are GBI, NINO1 + 2, NINO3 and NINO3.4, with correlations of 0.8, -0.8, -0.57 and -0.4, respectively. In terms of a previous step, the GBI, NINO1+2 and NINO3 indexes had the highest correlation values of 0.8, -0.8 and -0.5, respectively. The temporal pattern of the impact of some indicators, such as NINO, which was mostly strong and inversely in the same month, was directly and significantly in the two and three months earlier. Based on the results obtained from the multivariate modeling, the correlation between the selected teleconnection indexes such as GLOBAL MEAN TEMP, GBI, NINO 1+2 with thermal anomalies in the warm season of Iran are 0.94; as the best temperature predictions, and at the same time a month earlier, the NINO3 index was added to the above-mentioned indexes. In general, the indexes of NINO3-4, NINO3, NINO1+2, NINO4, and GBI are the best atmospheric and oceanographic indicators that predict Iran's temperature anomalies. 4. Conclusion According to numerical correlation analysis between the selective indexes and the temperature anomalies of the selective stations in the warm season in Iran showed that NINO3, NINO1 + 2, NINO3.4, NINO4, GBI and GLOBAL MEAN TEMPERATURE indexes are the most important oceanic-atmospheric predictors. Also, in this paper, linear regression functions for the relationship between indices and monthly temperature anomalies are presented, which can explain and predict the temperature changes in Iran. The correctness of these functions is confirmed by using the actual and modeled data (estimating R correlation values, RMSE and MBE values) with an acceptable error rate. It should be noted as long as the intervals of predicting are prolonged, apparently the importance of atmospheric indexes is reduced and contradictory the number and reliability of ocean indexes are increased. In total, using the above mentioned indices and using multivariate regression method in each step of time (simultaneously, one, two and three months earlier), the linear regression function for the relationship between indexes and monthly temperature anomalies of Iran has been presented, which by using it the Iran's temperature changes can be predicted finally. It should be noted that the functions obtained here are to predict the average temperature of selected stations in Iran, and therefore for each station the calculations must be made individually.
“Modeling The Relationship Between Teleconnection Indexes With Warm Season Temperature Anomalies In Iran Using Multivariate Regression” Metadata:
- Title: ➤ Modeling The Relationship Between Teleconnection Indexes With Warm Season Temperature Anomalies In Iran Using Multivariate Regression
- Author: ➤ The Journal of Geography and Environmental Hazards
- Language: per
“Modeling The Relationship Between Teleconnection Indexes With Warm Season Temperature Anomalies In Iran Using Multivariate Regression” Subjects and Themes:
- Subjects: ➤ Climate Perdition - Modeling - Multivariate Regression - Temperature Anomaly - climatic teleonnetion Index - Iran
Edition Identifiers:
- Internet Archive ID: ➤ geoeh-volume-6-issue-3-pages-47-66
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 11.06 Mbs, the file-s for this book were downloaded 7 times, the file-s went public at Fri Dec 20 2024.
Available formats:
Archive BitTorrent - DjVuTXT - Djvu XML - Item Tile - Metadata - OCR Page Index - OCR Search Text - Page Numbers JSON - Scandata - Single Page Processed JP2 ZIP - Text PDF - chOCR - hOCR -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Modeling The Relationship Between Teleconnection Indexes With Warm Season Temperature Anomalies In Iran Using Multivariate Regression at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
19Modeling And Control With Local Linearizing Nadaraya Watson Regression
By Steffen Kühn and Clemens Gühmann
Black box models of technical systems are purely descriptive. They do not explain why a system works the way it does. Thus, black box models are insufficient for some problems. But there are numerous applications, for example, in control engineering, for which a black box model is absolutely sufficient. In this article, we describe a general stochastic framework with which such models can be built easily and fully automated by observation. Furthermore, we give a practical example and show how this framework can be used to model and control a motorcar powertrain.
“Modeling And Control With Local Linearizing Nadaraya Watson Regression” Metadata:
- Title: ➤ Modeling And Control With Local Linearizing Nadaraya Watson Regression
- Authors: Steffen KühnClemens Gühmann
- Language: English
Edition Identifiers:
- Internet Archive ID: arxiv-0809.3690
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 6.43 Mbs, the file-s for this book were downloaded 67 times, the file-s went public at Wed Sep 18 2013.
Available formats:
Abbyy GZ - Animated GIF - Archive BitTorrent - DjVu - DjVuTXT - Djvu XML - Item Tile - Metadata - Scandata - Single Page Processed JP2 ZIP - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Modeling And Control With Local Linearizing Nadaraya Watson Regression at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
20Expert Trading Systems : Modeling Financial Markets With Kernel Regression
By Wolberg, John R
Black box models of technical systems are purely descriptive. They do not explain why a system works the way it does. Thus, black box models are insufficient for some problems. But there are numerous applications, for example, in control engineering, for which a black box model is absolutely sufficient. In this article, we describe a general stochastic framework with which such models can be built easily and fully automated by observation. Furthermore, we give a practical example and show how this framework can be used to model and control a motorcar powertrain.
“Expert Trading Systems : Modeling Financial Markets With Kernel Regression” Metadata:
- Title: ➤ Expert Trading Systems : Modeling Financial Markets With Kernel Regression
- Author: Wolberg, John R
- Language: English
“Expert Trading Systems : Modeling Financial Markets With Kernel Regression” Subjects and Themes:
- Subjects: ➤ Capital market -- Mathematical models - Expert systems (Computer Science) - Regression analysis
Edition Identifiers:
- Internet Archive ID: experttradingsys0000wolb
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 353.67 Mbs, the file-s for this book were downloaded 53 times, the file-s went public at Fri Jul 28 2023.
Available formats:
ACS Encrypted PDF - Cloth Cover Detection Log - DjVuTXT - Djvu XML - Dublin Core - Item Tile - JPEG Thumb - JSON - LCP Encrypted EPUB - LCP Encrypted PDF - Log - MARC - MARC Binary - Metadata - OCR Page Index - OCR Search Text - PNG - Page Numbers JSON - RePublisher Final Processing Log - RePublisher Initial Processing Log - Scandata - Single Page Original JP2 Tar - Single Page Processed JP2 ZIP - Text PDF - Title Page Detection Log - chOCR - hOCR -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Expert Trading Systems : Modeling Financial Markets With Kernel Regression at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
21Quantitative Prediction Of Integrase Inhibitor Resistance From Genotype Through Consensus Linear Regression Modeling.
By Van der Borght, Koen, Verheyen, Ann, Feyaerts, Maxim, Van Wesenbeeck, Liesbeth, Verlinden, Yvan, Van Craenenbroeck, Elke and van Vlijmen, Herman
This article is from Virology Journal , volume 10 . Abstract Background: Integrase inhibitors (INI) form a new drug class in the treatment of HIV-1 patients. We developed a linear regression modeling approach to make a quantitative raltegravir (RAL) resistance phenotype prediction, as Fold Change in IC50 against a wild type virus, from mutations in the integrase genotype. Methods: We developed a clonal genotype-phenotype database with 991 clones from 153 clinical isolates of INI naïve and RAL treated patients, and 28 site-directed mutants.We did the development of the RAL linear regression model in two stages, employing a genetic algorithm (GA) to select integrase mutations by consensus. First, we ran multiple GAs to generate first order linear regression models (GA models) that were stochastically optimized to reach a goal R2 accuracy, and consisted of a fixed-length subset of integrase mutations to estimate INI resistance. Secondly, we derived a consensus linear regression model in a forward stepwise regression procedure, considering integrase mutations or mutation pairs by descending prevalence in the GA models. Results: The most frequently occurring mutations in the GA models were 92Q, 97A, 143R and 155H (all 100%), 143G (90%), 148H/R (89%), 148K (88%), 151I (81%), 121Y (75%), 143C (72%), and 74M (69%). The RAL second order model contained 30 single mutations and five mutation pairs (p
“Quantitative Prediction Of Integrase Inhibitor Resistance From Genotype Through Consensus Linear Regression Modeling.” Metadata:
- Title: ➤ Quantitative Prediction Of Integrase Inhibitor Resistance From Genotype Through Consensus Linear Regression Modeling.
- Authors: ➤ Van der Borght, KoenVerheyen, AnnFeyaerts, MaximVan Wesenbeeck, LiesbethVerlinden, YvanVan Craenenbroeck, Elkevan Vlijmen, Herman
- Language: English
Edition Identifiers:
- Internet Archive ID: pubmed-PMC3551713
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 10.98 Mbs, the file-s for this book were downloaded 68 times, the file-s went public at Fri Oct 24 2014.
Available formats:
Abbyy GZ - Animated GIF - Archive BitTorrent - DjVu - DjVuTXT - Djvu XML - Item Tile - JSON - Metadata - Scandata - Single Page Processed JP2 ZIP - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Quantitative Prediction Of Integrase Inhibitor Resistance From Genotype Through Consensus Linear Regression Modeling. at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
22Voxelwise Nonlinear Regression Toolbox For Neuroimage Analysis: Application To Aging And Neurodegenerative Disease Modeling
By Santi Puch, Asier Aduriz, Adrià Casamitjana, Veronica Vilaplana, Paula Petrone, Grégory Operto, Raffaele Cacciaglia, Stavros Skouras, Carles Falcon, José Luis Molinuevo and Juan Domingo Gispert
This paper describes a new neuroimaging analysis toolbox that allows for the modeling of nonlinear effects at the voxel level, overcoming limitations of methods based on linear models like the GLM. We illustrate its features using a relevant example in which distinct nonlinear trajectories of Alzheimer's disease related brain atrophy patterns were found across the full biological spectrum of the disease. The open-source toolbox presented in this paper is available at https://github.com/imatge-upc/VNeAT.
“Voxelwise Nonlinear Regression Toolbox For Neuroimage Analysis: Application To Aging And Neurodegenerative Disease Modeling” Metadata:
- Title: ➤ Voxelwise Nonlinear Regression Toolbox For Neuroimage Analysis: Application To Aging And Neurodegenerative Disease Modeling
- Authors: ➤ Santi PuchAsier AdurizAdrià CasamitjanaVeronica VilaplanaPaula PetroneGrégory OpertoRaffaele CacciagliaStavros SkourasCarles FalconJosé Luis MolinuevoJuan Domingo Gispert
“Voxelwise Nonlinear Regression Toolbox For Neuroimage Analysis: Application To Aging And Neurodegenerative Disease Modeling” Subjects and Themes:
- Subjects: ➤ Computer Vision and Pattern Recognition - Machine Learning - Applications - Neurons and Cognition - Learning - Statistics - Quantitative Biology - Computing Research Repository
Edition Identifiers:
- Internet Archive ID: arxiv-1612.00667
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 1.68 Mbs, the file-s for this book were downloaded 19 times, the file-s went public at Fri Jun 29 2018.
Available formats:
Archive BitTorrent - Metadata - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Voxelwise Nonlinear Regression Toolbox For Neuroimage Analysis: Application To Aging And Neurodegenerative Disease Modeling at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
23DTIC ADA246155: An Investigation Of Multivariate Adaptive Regression Splines For Modeling And Analysis Of Univariate And Semi-Multivariate Time Series Systems
By Defense Technical Information Center
This dissertation investigates the use of multivariate adaptive regression splines (MARS), due to Friedman, for nonlinear regression modeling and analysis of time series systems. MARS can be conceptualized as a generalization of recursive partitioning that use spline fitting in lieu of other simple fitting functions. MARS is a computationally intensive methodology that fits a nonparametric regression model in the form of an expansion in product spline basis functions of predictor variables chosen during a forward and backward recursive partitioning strategy. The MARS algorithm produces continuous nonlinear regression models for high-dimensional data using a combination of predictor variable interactions and partitions of the predictor variable space. By letting the predictor variables in the MARS algorithm be lagged values of a time series system, one obtains a univariate (ASTAR) or semi- multivariate (SMASTAR) adaptive spline threshold autoregressive model for nonlinear autoregressive threshold modeling and analysis of time series, thereby extending the threshold autoregression (TAR) time series methodology developed by Tong.
“DTIC ADA246155: An Investigation Of Multivariate Adaptive Regression Splines For Modeling And Analysis Of Univariate And Semi-Multivariate Time Series Systems” Metadata:
- Title: ➤ DTIC ADA246155: An Investigation Of Multivariate Adaptive Regression Splines For Modeling And Analysis Of Univariate And Semi-Multivariate Time Series Systems
- Author: ➤ Defense Technical Information Center
- Language: English
“DTIC ADA246155: An Investigation Of Multivariate Adaptive Regression Splines For Modeling And Analysis Of Univariate And Semi-Multivariate Time Series Systems” Subjects and Themes:
- Subjects: ➤ DTIC Archive - Stevens, James G - NAVAL POSTGRADUATE SCHOOL MONTEREY CA - *REGRESSION ANALYSIS - *MULTIVARIATE ANALYSIS - *SPLINES - INTERACTIONS - THRESHOLD EFFECTS - TIME SERIES ANALYSIS - THESES - VARIABLES - NONPARAMETRIC STATISTICS - NONLINEAR SYSTEMS - ADAPTIVE SYSTEMS - NONLINEAR ANALYSIS - CONTINUITY - FITTING FUNCTIONS(MATHEMATICS) - FITTINGS - MODELS - PREDICTIONS - MATHEMATICAL MODELS - METHODOLOGY
Edition Identifiers:
- Internet Archive ID: DTIC_ADA246155
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 120.87 Mbs, the file-s for this book were downloaded 83 times, the file-s went public at Sun Mar 04 2018.
Available formats:
Abbyy GZ - Archive BitTorrent - DjVuTXT - Djvu XML - Item Tile - Metadata - OCR Page Index - OCR Search Text - Page Numbers JSON - Scandata - Single Page Processed JP2 ZIP - Text PDF - chOCR - hOCR -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find DTIC ADA246155: An Investigation Of Multivariate Adaptive Regression Splines For Modeling And Analysis Of Univariate And Semi-Multivariate Time Series Systems at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
24An Investigation Of Multivariate Adaptive Regression Splines For Modeling And Analysis Of Univariate And Semi-multivariate Time Series Systems.
By Stevens, James G.;Lewis, Peter A. W.
This dissertation investigates the use of multivariate adaptive regression splines (MARS), due to Friedman, for nonlinear regression modeling and analysis of time series systems. MARS can be conceptualized as a generalization of recursive partitioning that use spline fitting in lieu of other simple fitting functions. MARS is a computationally intensive methodology that fits a nonparametric regression model in the form of an expansion in product spline basis functions of predictor variables chosen during a forward and backward recursive partitioning strategy. The MARS algorithm produces continuous nonlinear regression models for high-dimensional data using a combination of predictor variable interactions and partitions of the predictor variable space. By letting the predictor variables in the MARS algorithm be lagged values of a time series system, one obtains a univariate (ASTAR) or semi- multivariate (SMASTAR) adaptive spline threshold autoregressive model for nonlinear autoregressive threshold modeling and analysis of time series, thereby extending the threshold autoregression (TAR) time series methodology developed by Tong.
“An Investigation Of Multivariate Adaptive Regression Splines For Modeling And Analysis Of Univariate And Semi-multivariate Time Series Systems.” Metadata:
- Title: ➤ An Investigation Of Multivariate Adaptive Regression Splines For Modeling And Analysis Of Univariate And Semi-multivariate Time Series Systems.
- Author: ➤ Stevens, James G.;Lewis, Peter A. W.
- Language: en_US
Edition Identifiers:
- Internet Archive ID: investigationofm00stev
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 283.21 Mbs, the file-s for this book were downloaded 354 times, the file-s went public at Wed Nov 28 2012.
Available formats:
Abbyy GZ - Animated GIF - Archive BitTorrent - Cloth Cover Detection Log - Contents - DjVu - DjVuTXT - Djvu XML - Dublin Core - JPEG Thumb - MARC - MARC Binary - MARC Source - Metadata - OCR Page Index - OCR Search Text - Page Numbers JSON - Scandata - Single Page Original JP2 Tar - Single Page Processed JP2 ZIP - Text PDF - chOCR - hOCR -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find An Investigation Of Multivariate Adaptive Regression Splines For Modeling And Analysis Of Univariate And Semi-multivariate Time Series Systems. at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
25Regression With Social Data : Modeling Continuous And Limited Response Variables
By DeMaris, Alfred, 1946-
This dissertation investigates the use of multivariate adaptive regression splines (MARS), due to Friedman, for nonlinear regression modeling and analysis of time series systems. MARS can be conceptualized as a generalization of recursive partitioning that use spline fitting in lieu of other simple fitting functions. MARS is a computationally intensive methodology that fits a nonparametric regression model in the form of an expansion in product spline basis functions of predictor variables chosen during a forward and backward recursive partitioning strategy. The MARS algorithm produces continuous nonlinear regression models for high-dimensional data using a combination of predictor variable interactions and partitions of the predictor variable space. By letting the predictor variables in the MARS algorithm be lagged values of a time series system, one obtains a univariate (ASTAR) or semi- multivariate (SMASTAR) adaptive spline threshold autoregressive model for nonlinear autoregressive threshold modeling and analysis of time series, thereby extending the threshold autoregression (TAR) time series methodology developed by Tong.
“Regression With Social Data : Modeling Continuous And Limited Response Variables” Metadata:
- Title: ➤ Regression With Social Data : Modeling Continuous And Limited Response Variables
- Author: DeMaris, Alfred, 1946-
- Language: English
“Regression With Social Data : Modeling Continuous And Limited Response Variables” Subjects and Themes:
- Subjects: ➤ Statistique -- Methodologie - Regression analysis Psychological methods, research statistics Applied probability and statistics - Social sciences -- Statistics -- Methodology - Regression analysis - Analyse de régression - Statistics -- Methodology - Statistique -- Méthodologie - Sciences sociales -- Statistiques -- Méthodologie - MATHEMATICS -- Probability & Statistics -- Regression Analysis - Sciences sociales -- Statistiques -- Methodologie - Analyse de regression
Edition Identifiers:
- Internet Archive ID: regressionwithso0000dema
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 1023.55 Mbs, the file-s for this book were downloaded 33 times, the file-s went public at Thu May 07 2020.
Available formats:
ACS Encrypted EPUB - ACS Encrypted PDF - Abbyy GZ - Cloth Cover Detection Log - DjVuTXT - Djvu XML - Dublin Core - Item Tile - JPEG Thumb - JSON - LCP Encrypted EPUB - LCP Encrypted PDF - Log - MARC - MARC Binary - Metadata - OCR Page Index - OCR Search Text - PNG - Page Numbers JSON - Scandata - Single Page Original JP2 Tar - Single Page Processed JP2 ZIP - Text PDF - Title Page Detection Log - chOCR - hOCR -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Regression With Social Data : Modeling Continuous And Limited Response Variables at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
26Determining Late Predictors Of Outcome For Acetaminophen- Induced Acute Liver Failure Using Classification And Regression Tree Modeling Analysis.
By Karvellas, C, Speiser, J and Lee, W
This article is from Critical Care , volume 18 . Abstract None
“Determining Late Predictors Of Outcome For Acetaminophen- Induced Acute Liver Failure Using Classification And Regression Tree Modeling Analysis.” Metadata:
- Title: ➤ Determining Late Predictors Of Outcome For Acetaminophen- Induced Acute Liver Failure Using Classification And Regression Tree Modeling Analysis.
- Authors: Karvellas, CSpeiser, JLee, W
- Language: English
Edition Identifiers:
- Internet Archive ID: pubmed-PMC4068393
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 177.50 Mbs, the file-s for this book were downloaded 80 times, the file-s went public at Fri Oct 17 2014.
Available formats:
Abbyy GZ - Animated GIF - Archive BitTorrent - DjVu - DjVuTXT - Djvu XML - Item Tile - JSON - Metadata - Scandata - Single Page Processed JP2 ZIP - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Determining Late Predictors Of Outcome For Acetaminophen- Induced Acute Liver Failure Using Classification And Regression Tree Modeling Analysis. at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
27Regression Modeling Of The North East Atlantic Spring Bloom Suggests Previously Unrecognized Biological Roles For V And Mo.
By Klein, Nick J., Beck, A. J., Hutchins, D. A. and Sanudo-Wilhelmy, S. A.
This article is from Frontiers in Microbiology , volume 4 . Abstract In order to identify the biogeochemical parameters controlling pCO2, total chlorophyll a, and dimethyl sulfide (DMS) concentrations during the North East Atlantic Spring Bloom (NASB), we used previously unpublished particulate and dissolved elemental concentrations to construct several linear regression models; first by hypothesis-testing, and then with exhaustive stepwise linear regression followed by leave-one-out cross-validation. The field data was obtained along a latitudinal transect from the Azores Islands to the North Atlantic, and best-fit models (determined by lowest predictive error) of up to three variables are presented. Total chlorophyll a is predicted best by biomass (POC, PON) parameters and by pigments characteristic of picophytoplankton for the southern section of the sampling transect (from the Azores to the Rockhall-Hatton Plateau) and coccolithophores in the northern portion (from the Rockhall-Hatton Plateau to the Denmark Strait). Both the pCO2 and DMS models included variables traditionally associated with the development of the NASB such as mixed-layer depth and with Fe, Si, and P-deplete conditions (dissolved Fe, dissolved and biogenic silica, dissolved PO3−4). However, the regressions for pCO2 and DMS also include intracellular V and Mo concentrations, respectively. Mo is involved in DMS production as a cofactor in dimethylsulfoxide reductase. No significant biological role for V has yet been determined, although intracellular V is significantly correlated (p-value
“Regression Modeling Of The North East Atlantic Spring Bloom Suggests Previously Unrecognized Biological Roles For V And Mo.” Metadata:
- Title: ➤ Regression Modeling Of The North East Atlantic Spring Bloom Suggests Previously Unrecognized Biological Roles For V And Mo.
- Authors: Klein, Nick J.Beck, A. J.Hutchins, D. A.Sanudo-Wilhelmy, S. A.
- Language: English
Edition Identifiers:
- Internet Archive ID: pubmed-PMC3591785
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 10.39 Mbs, the file-s for this book were downloaded 72 times, the file-s went public at Wed Oct 29 2014.
Available formats:
Abbyy GZ - Animated GIF - Archive BitTorrent - DjVu - DjVuTXT - Djvu XML - Item Tile - JSON - Metadata - Scandata - Single Page Processed JP2 ZIP - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Regression Modeling Of The North East Atlantic Spring Bloom Suggests Previously Unrecognized Biological Roles For V And Mo. at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
28ERIC ED531719: Analyzing Multilevel Data: An Empirical Comparison Of Parameter Estimates Of Hierarchical Linear Modeling And Ordinary Least Squares Regression
By ERIC
Hierarchical linear models (HLM) solve the problems associated with the unit of analysis problem such as misestimated standard errors, heterogeneity of regression and aggregation bias by modeling all levels of interest simultaneously. Hierarchical linear modeling resolves the problem of misestimated standard errors by incorporating a unique random effect for each institution into the statistical model; moreover, the variability in these random effects is taken into account in estimating the standard errors. Until the advent of HLM, heterogeneity of regression had often been viewed as a methodological nuisance. However, the cause of heterogeneity of regression is often of substantive interest. HLMs enable a researcher to estimate a separate set of regression coefficients for each higher level organizational unit and then model variation among the higher level units in their sets of coefficients as multivariate outcomes to be explained by higher level factors. HLMs solve the problem of aggregation bias by modeling each level of the hierarchy with its own model. Today, many higher education scholars are rushing to use this new, sophisticated analytic procedure. This rush seems to be based on the assumption that HLM might yield substantively different findings than those from studies based on ordinary least squares (OLS) regression analyses. With this in mind, the current study investigates the different conclusions that may be drawn depending upon the type of analysis chosen. This paper focuses on the three types of analyses discussed above. The first analysis will be an OLS regression with the student as the unit of analysis, the second analysis will be an OLS regression with the student level variables aggregated to the institutional level with the institution as the unit of analysis, and the third analysis will be a three-level hierarchical linear model with student characteristics modeled at Level 1, characteristics about the major modeled at Level 2 and characteristics of the institution modeled at Level 3. Appended are: (1) Items comprising the variables used in the analyses and the construction of scales; and (2) List of Majors and Biglan (1973a, 1973b) classification. (Contains 8 tables.)
“ERIC ED531719: Analyzing Multilevel Data: An Empirical Comparison Of Parameter Estimates Of Hierarchical Linear Modeling And Ordinary Least Squares Regression” Metadata:
- Title: ➤ ERIC ED531719: Analyzing Multilevel Data: An Empirical Comparison Of Parameter Estimates Of Hierarchical Linear Modeling And Ordinary Least Squares Regression
- Author: ERIC
- Language: English
“ERIC ED531719: Analyzing Multilevel Data: An Empirical Comparison Of Parameter Estimates Of Hierarchical Linear Modeling And Ordinary Least Squares Regression” Subjects and Themes:
- Subjects: ➤ ERIC Archive - Regression (Statistics) - Models - Least Squares Statistics - Data Analysis - Comparative Analysis - Computation - College Seniors - Student Characteristics - Institutional Characteristics - Majors (Students) - Critical Thinking - Thinking Skills - Rocconi, Louis M.
Edition Identifiers:
- Internet Archive ID: ERIC_ED531719
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 25.37 Mbs, the file-s for this book were downloaded 63 times, the file-s went public at Wed Feb 17 2016.
Available formats:
Abbyy GZ - Animated GIF - Archive BitTorrent - DjVu - DjVuTXT - Djvu XML - Item Tile - Metadata - Scandata - Single Page Processed JP2 ZIP - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find ERIC ED531719: Analyzing Multilevel Data: An Empirical Comparison Of Parameter Estimates Of Hierarchical Linear Modeling And Ordinary Least Squares Regression at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
29Prediction And Modeling Of Dry Seasons Air Pollution Changes Using Multiple Linear Regression Model: A Case Study Of Port Harcourt And Its Environs, Niger Delta, Nigeria
The influence of meteorological parameters on air pollutants over Port Harcourt and its environs in the dry season was modeled using multiple linear regressions model. Results indicated that meteorological parameters significantly influenced pollutant concentrations; results also showed poor linear relationships between meteorological parameters and pollutant concentrations, and that meteorological parameters are poor predictor variables of concentrations of air pollutants in the area. Pollution roses of pollutants dispersion pattern in the study area showed that pollutant concentrations increase with increased wind speed. Result also showed that wind speed exerts positive influence on the concentration levels of pollutants in the study area. The yearly prediction of air pollutants was also carried out using a ten-year data from previous studies conducted in the study area. The prediction was done using regression analysis and year as the predictor variable to develop a model. The relationship between air pollutants and year was therefore established for the annual prediction of the future pollutant concentrations in the dry seasons for period of the next fifteen years.
“Prediction And Modeling Of Dry Seasons Air Pollution Changes Using Multiple Linear Regression Model: A Case Study Of Port Harcourt And Its Environs, Niger Delta, Nigeria” Metadata:
- Title: ➤ Prediction And Modeling Of Dry Seasons Air Pollution Changes Using Multiple Linear Regression Model: A Case Study Of Port Harcourt And Its Environs, Niger Delta, Nigeria
- Language: English
“Prediction And Modeling Of Dry Seasons Air Pollution Changes Using Multiple Linear Regression Model: A Case Study Of Port Harcourt And Its Environs, Niger Delta, Nigeria” Subjects and Themes:
- Subjects: ➤ Multiple linear Regressions Model - Air Pollution changes - Meteorological variables concentration.
Edition Identifiers:
- Internet Archive ID: 25Prediction
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 19.79 Mbs, the file-s for this book were downloaded 116 times, the file-s went public at Thu Jun 14 2018.
Available formats:
Abbyy GZ - Archive BitTorrent - DjVuTXT - Djvu XML - JPEG Thumb - Metadata - Scandata - Single Page Processed JP2 ZIP - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Prediction And Modeling Of Dry Seasons Air Pollution Changes Using Multiple Linear Regression Model: A Case Study Of Port Harcourt And Its Environs, Niger Delta, Nigeria at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
30Bayesian Nonparametric Modeling For Mean Residual Life Regression
By Valerie Poynor and Athanasios Kottas
The mean residual life function is a key functional for a survival distribution. It has practically useful interpretation as the expected remaining lifetime given survival up to a particular time point, and it also characterizes the survival distribution. However, it has received limited attention in terms of inference methods under a probabilistic modeling framework. In this paper, we seek to provide general inference methodology for mean residual life regression. Survival data often include a set of predictor variables for the survival response distribution, and in many cases it is natural to include the covariates as random variables into the modeling. We thus propose a Dirichlet process mixture modeling approach for the joint stochastic mechanism of the covariates and survival responses. This approach implies a flexible model structure for the mean residual life of the conditional response distribution, allowing general shapes for mean residual life as a function of covariates given a specific time point, as well as a function of time given particular values of the covariate vector. To expand the scope of the modeling framework, we extend the mixture model to incorporate dependence across experimental groups, such as treatment and control groups. This extension is built from a dependent Dirichlet process prior for the group-specific mixing distributions, with common locations and weights that vary across groups through latent bivariate beta distributed random variables. We develop properties of the proposed regression models, and discuss methods for prior specification and posterior inference. The different components of the methodology are illustrated with simulated data sets. Moreover, the modeling approach is applied to a data set comprising right censored survival times of patients with small cell lung cancer.
“Bayesian Nonparametric Modeling For Mean Residual Life Regression” Metadata:
- Title: ➤ Bayesian Nonparametric Modeling For Mean Residual Life Regression
- Authors: Valerie PoynorAthanasios Kottas
“Bayesian Nonparametric Modeling For Mean Residual Life Regression” Subjects and Themes:
- Subjects: Applications - Statistics - Methodology
Edition Identifiers:
- Internet Archive ID: arxiv-1412.0367
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 7.52 Mbs, the file-s for this book were downloaded 17 times, the file-s went public at Sat Jun 30 2018.
Available formats:
Archive BitTorrent - Metadata - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Bayesian Nonparametric Modeling For Mean Residual Life Regression at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
31Crash Severity Modeling Using Multinomial Logistic Regression
By Azad Abdulhafedh
Incorporating the Multinomial Logistic Regression in Vehicle Crash Severity Modeling: A Detailed Overview
“Crash Severity Modeling Using Multinomial Logistic Regression” Metadata:
- Title: ➤ Crash Severity Modeling Using Multinomial Logistic Regression
- Author: Azad Abdulhafedh
- Language: English
“Crash Severity Modeling Using Multinomial Logistic Regression” Subjects and Themes:
- Subjects: ➤ Multinomial Logistic Regression - Odd Ratio - The Independence of Irrelevant Alternatives - The Hausman Specification Test - The Hosmer-Lemeshow Test - Pseudo R Squares - Crash Severity Models
Edition Identifiers:
- Internet Archive ID: ➤ crash-severity-modeling-using-multinomial-logistic-regression_202308
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 15.02 Mbs, the file-s for this book were downloaded 38 times, the file-s went public at Thu Aug 17 2023.
Available formats:
Archive BitTorrent - DjVuTXT - Djvu XML - Item Tile - Metadata - OCR Page Index - OCR Search Text - Page Numbers JSON - Scandata - Single Page Processed JP2 ZIP - Text PDF - chOCR - hOCR -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Crash Severity Modeling Using Multinomial Logistic Regression at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
32DTIC ADA332822: Input Variable Selection For Non-Parametric Regression, Classification, And Time Series Modeling.
By Defense Technical Information Center
Variable selection is a critical step in constructing statistical regression, pattern classification, or time series models that are capable of optimum generalization performance. Since the project got started in February 1996, we have implemented the prototype K-test as proposed, carried out extensive testing on regression and time series problems, and developed a selection criterion based upon unsupervised clustering methods. The latter can be applied to both regression and classification type problems. Under ONR sponsorship, a number of criterion functions have been devised and tested for developing the variable selection methodologies. The work on this project has been conducted by Hong Pi and John Moody. Since Hong Pi has taken a job in industry, Howard Yang (from Amari's research group in Tokyo) will continue working on the project in place of Hong.
“DTIC ADA332822: Input Variable Selection For Non-Parametric Regression, Classification, And Time Series Modeling.” Metadata:
- Title: ➤ DTIC ADA332822: Input Variable Selection For Non-Parametric Regression, Classification, And Time Series Modeling.
- Author: ➤ Defense Technical Information Center
- Language: English
“DTIC ADA332822: Input Variable Selection For Non-Parametric Regression, Classification, And Time Series Modeling.” Subjects and Themes:
- Subjects: ➤ DTIC Archive - Moody, John E. - OFFICE OF NAVAL RESEARCH ARLINGTON VA - *STATISTICS - *TIME SERIES ANALYSIS - *REGRESSION ANALYSIS - MATHEMATICAL MODELS - INPUT - VARIABLES - CLUSTERING - CLASSIFICATION - COLLECTING METHODS.
Edition Identifiers:
- Internet Archive ID: DTIC_ADA332822
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 10.23 Mbs, the file-s for this book were downloaded 42 times, the file-s went public at Mon Apr 09 2018.
Available formats:
Abbyy GZ - Additional Text PDF - Archive BitTorrent - DjVuTXT - Djvu XML - Image Container PDF - JPEG Thumb - Metadata - OCR Page Index - OCR Search Text - Page Numbers JSON - Scandata - Single Page Processed JP2 ZIP - chOCR - hOCR -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find DTIC ADA332822: Input Variable Selection For Non-Parametric Regression, Classification, And Time Series Modeling. at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
33DTIC ADA126840: Non-Linear Regression Analysis: A General Program For Data Modeling Using Personal Microcomputers
By Defense Technical Information Center
This report documents a general non-linear regression program for fitting data to non-linear models. The program is based on an algorithm which uses a least squares criterion to calculate successive improvements to an initial set of parameter estimates. The program is written in the BASIC language common to most microcomputers, because it is easy to use and to transport between machines from different manufacturers. The majority of inexpensive microcomputers do not offer matrix operations as part of their BASIC interpreter. The program presented here, therefore, supplies subroutines in BASIC for the zeroing, transposing and inverting of the required matrices, to make it compatible with most microcomputers available today. The report gives examples and program output based on a demonstration data set involving antigen- antibody complexation in solution. Two derivations of function subroutines are given to assist the user in developing his own functions. A complete listing of the necessary programs is given along with a section on program cautious.
“DTIC ADA126840: Non-Linear Regression Analysis: A General Program For Data Modeling Using Personal Microcomputers” Metadata:
- Title: ➤ DTIC ADA126840: Non-Linear Regression Analysis: A General Program For Data Modeling Using Personal Microcomputers
- Author: ➤ Defense Technical Information Center
- Language: English
“DTIC ADA126840: Non-Linear Regression Analysis: A General Program For Data Modeling Using Personal Microcomputers” Subjects and Themes:
- Subjects: ➤ DTIC Archive - Nelson, Dennis P. - NAVAL HEALTH RESEARCH CENTER SAN DIEGO CA - *COMPUTER PROGRAMS - *REGRESSION ANALYSIS - *MICROCOMPUTERS - *NONLINEAR ANALYSIS - *ANTIGEN ANTIBODY REACTIONS - MATHEMATICAL MODELS - ALGORITHMS - COMPUTATIONS - PARAMETERS - MATRICES(MATHEMATICS) - ESTIMATES - LEAST SQUARES METHOD - SUBROUTINES - FITTING FUNCTIONS(MATHEMATICS).
Edition Identifiers:
- Internet Archive ID: DTIC_ADA126840
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 23.19 Mbs, the file-s for this book were downloaded 64 times, the file-s went public at Wed Jan 10 2018.
Available formats:
Abbyy GZ - Archive BitTorrent - DjVuTXT - Djvu XML - Item Tile - Metadata - OCR Page Index - OCR Search Text - Page Numbers JSON - Scandata - Single Page Processed JP2 ZIP - Text PDF - chOCR - hOCR -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find DTIC ADA126840: Non-Linear Regression Analysis: A General Program For Data Modeling Using Personal Microcomputers at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
34Spatial Bayesian Latent Factor Regression Modeling Of Coordinate-based Meta-analysis Data
By Silvia Montagna, Tor Wager, Lisa Feldman-Barrett, Timothy D. Johnson and Thomas E. Nichols
Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the paper are available for Coordinate-based Meta-analysis (CBMA). Neuroimaging meta-analysis is used to 1) identify areas of consistent activation; and 2) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterised as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and a neuroimaging meta-analysis dataset.
“Spatial Bayesian Latent Factor Regression Modeling Of Coordinate-based Meta-analysis Data” Metadata:
- Title: ➤ Spatial Bayesian Latent Factor Regression Modeling Of Coordinate-based Meta-analysis Data
- Authors: Silvia MontagnaTor WagerLisa Feldman-BarrettTimothy D. JohnsonThomas E. Nichols
“Spatial Bayesian Latent Factor Regression Modeling Of Coordinate-based Meta-analysis Data” Subjects and Themes:
- Subjects: Methodology - Statistics
Edition Identifiers:
- Internet Archive ID: arxiv-1606.06912
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 1.43 Mbs, the file-s for this book were downloaded 20 times, the file-s went public at Fri Jun 29 2018.
Available formats:
Archive BitTorrent - Metadata - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Spatial Bayesian Latent Factor Regression Modeling Of Coordinate-based Meta-analysis Data at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
35Modeling Compositional Regression With Uncorrelated And Correlated Errors: A Bayesian Approach
By Taciana K. O. Shimizu, Francisco Louzada, Adriano K. Suzuki and Ricardo S. Ehlers
Compositional data consist of known compositions vectors whose components are positive and defined in the interval (0,1) representing proportions or fractions of a "whole". The sum of these components must be equal to one. Compositional data is present in different knowledge areas, as in geology, economy, medicine among many others. In this paper, we introduce a Bayesian analysis for compositional regression applying additive log-ratio (ALR) transformation and assuming uncorrelated and correlated errors. The Bayesian inference procedure based on Markov Chain Monte Carlo Methods (MCMC). The methodology is illustrated on an artificial and a real data set of volleyball.
“Modeling Compositional Regression With Uncorrelated And Correlated Errors: A Bayesian Approach” Metadata:
- Title: ➤ Modeling Compositional Regression With Uncorrelated And Correlated Errors: A Bayesian Approach
- Authors: Taciana K. O. ShimizuFrancisco LouzadaAdriano K. SuzukiRicardo S. Ehlers
- Language: English
“Modeling Compositional Regression With Uncorrelated And Correlated Errors: A Bayesian Approach” Subjects and Themes:
- Subjects: Statistics - Applications
Edition Identifiers:
- Internet Archive ID: arxiv-1507.00225
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 6.36 Mbs, the file-s for this book were downloaded 35 times, the file-s went public at Thu Jun 28 2018.
Available formats:
Abbyy GZ - Archive BitTorrent - DjVuTXT - Djvu XML - JPEG Thumb - Metadata - Scandata - Single Page Processed JP2 ZIP - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Modeling Compositional Regression With Uncorrelated And Correlated Errors: A Bayesian Approach at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
36Comment: Struggles With Survey Weighting And Regression Modeling
By Sharon L. Lohr
Comment: Struggles with Survey Weighting and Regression Modeling [arXiv:0710.5005]
“Comment: Struggles With Survey Weighting And Regression Modeling” Metadata:
- Title: ➤ Comment: Struggles With Survey Weighting And Regression Modeling
- Author: Sharon L. Lohr
- Language: English
Edition Identifiers:
- Internet Archive ID: arxiv-0710.5015
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 3.35 Mbs, the file-s for this book were downloaded 66 times, the file-s went public at Mon Sep 23 2013.
Available formats:
Abbyy GZ - Animated GIF - Archive BitTorrent - DjVu - DjVuTXT - Djvu XML - Item Tile - Metadata - Scandata - Single Page Processed JP2 ZIP - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Comment: Struggles With Survey Weighting And Regression Modeling at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
37Modeling Of Ankle Joint Range Of Motion And Landing Quality Scores In Female Soccer Players With Quantile Regression Approach
By Mohammad Alimoradi, Bogdan Antohe and Huseyin Sahin Uysal
Football is recognized worldwide as a mainstream sport and is becoming increasingly attractive to women's participation. Research has shown that women's football has experienced rapid growth spanning professional, semi-professional, and amateur levels and is considered the fastest-growing sport in the world (Rosso, 2009). While knee, ankle, shoulder, and head/neck injuries are common in football, studies have reported that 86.8% of injuries related to this issue arise from lower extremity joints such as ankle and knee (Martín-San Agustín et al., 2021). The ankle, the lowest component of the kinetic chain in the lower extremity, can be negatively affected by sprains, Achilles tendon injuries, and plantar fascia injuries that occur during competition (Zellers et al., 2021). These external factors can limit ankle range of motion (ROM), and limited ankle range of motion can also be identified as an essential risk factor for anterior cruciate ligament (ACL) injury (Amraee et al., 2017). Although various protocols have been developed for landing techniques, a general landing technique testing protocol may not be suitable for interpreting sport-specific mechanical actions. For example, football players may focus on multiple tasks during a jumping motion, including both ball and landing mechanics. However, current landing technique tests may not be sufficient to interpret sport-specific landing quality (Akbari et al., 2023). REFERENCE Rosso, E. (2009). From informal recreation to a geography of achievement: Women’s soccer in south australia. Geographical Research, 48(2), 181–196. https://doi.org/10.1111/j.1745-5871.2009.00618.x Martín-San Agustín, R., Medina-Mirapeix, F., Esteban-Catalán, A., Escriche-Escuder, A., Sánchez-Barbadora, M., & Benítez-Martínez, J. C. (2021). Epidemiology of Injuries in First Division Spanish Women’s Soccer Players. International Journal of Environmental Research and Public Health, 18(6), 3009. https://doi.org/10.3390/ijerph18063009 Zellers, J. A., Baxter, J. R., & Grävare Silbernagel, K. (2021). Functional Ankle Range of Motion but Not Peak Achilles Tendon Force Diminished With Heel-Rise and Jumping Tasks After Achilles Tendon Repair. The American Journal of Sports Medicine, 49(9), 2439–2446. https://doi.org/10.1177/03635465211019436 Amraee, D., Alizadeh, M. H., Minoonejhad, H., Razi, M., & Amraee, G. H. (2017). Predictor factors for lower extremity malalignment and non-contact anterior cruciate ligament injuries in male athletes. Knee Surgery, Sports Traumatology, Arthroscopy, 25(5), 1625–1631. https://doi.org/10.1007/s00167-015-3926-8
“Modeling Of Ankle Joint Range Of Motion And Landing Quality Scores In Female Soccer Players With Quantile Regression Approach” Metadata:
- Title: ➤ Modeling Of Ankle Joint Range Of Motion And Landing Quality Scores In Female Soccer Players With Quantile Regression Approach
- Authors: Mohammad AlimoradiBogdan AntoheHuseyin Sahin Uysal
Edition Identifiers:
- Internet Archive ID: osf-registrations-kp7xa-v1
Downloads Information:
The book is available for download in "data" format, the size of the file-s is: 0.12 Mbs, the file-s for this book were downloaded 1 times, the file-s went public at Mon Dec 11 2023.
Available formats:
Archive BitTorrent - Metadata - ZIP -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Modeling Of Ankle Joint Range Of Motion And Landing Quality Scores In Female Soccer Players With Quantile Regression Approach at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
38A General Approach To Statistical Modeling Of Physical Laws: Nonparametric Regression
By I. Grabec
Statistical modeling of experimental physical laws is based on the probability density function of measured variables. It is expressed by experimental data via a kernel estimator. The kernel is determined objectively by the scattering of data during calibration of experimental setup. A physical law, which relates measured variables, is optimally extracted from experimental data by the conditional average estimator. It is derived directly from the kernel estimator and corresponds to a general nonparametric regression. The proposed method is demonstrated by the modeling of a return map of noisy chaotic data. In this example, the nonparametric regression is used to predict a future value of chaotic time series from the present one. The mean predictor error is used in the definition of predictor quality, while the redundancy is expressed by the mean square distance between data points. Both statistics are used in a new definition of predictor cost function. From the minimum of the predictor cost function, a proper number of data in the model is estimated.
“A General Approach To Statistical Modeling Of Physical Laws: Nonparametric Regression” Metadata:
- Title: ➤ A General Approach To Statistical Modeling Of Physical Laws: Nonparametric Regression
- Author: I. Grabec
- Language: English
Edition Identifiers:
- Internet Archive ID: arxiv-0704.0089
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 8.15 Mbs, the file-s for this book were downloaded 85 times, the file-s went public at Wed Sep 18 2013.
Available formats:
Abbyy GZ - Animated GIF - Archive BitTorrent - DjVu - DjVuTXT - Djvu XML - Item Tile - Metadata - Scandata - Single Page Processed JP2 ZIP - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find A General Approach To Statistical Modeling Of Physical Laws: Nonparametric Regression at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
39Comment: Struggles With Survey Weighting And Regression Modeling
By Danny Pfeffermann
Comment: Struggles with Survey Weighting and Regression Modeling [arXiv:0710.5005]
“Comment: Struggles With Survey Weighting And Regression Modeling” Metadata:
- Title: ➤ Comment: Struggles With Survey Weighting And Regression Modeling
- Author: Danny Pfeffermann
- Language: English
Edition Identifiers:
- Internet Archive ID: arxiv-0710.5016
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 4.19 Mbs, the file-s for this book were downloaded 69 times, the file-s went public at Mon Sep 23 2013.
Available formats:
Abbyy GZ - Animated GIF - Archive BitTorrent - DjVu - DjVuTXT - Djvu XML - JPEG Thumb - Metadata - Scandata - Single Page Processed JP2 ZIP - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Comment: Struggles With Survey Weighting And Regression Modeling at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
40Struggles With Survey Weighting And Regression Modeling
By Andrew Gelman
The general principles of Bayesian data analysis imply that models for survey responses should be constructed conditional on all variables that affect the probability of inclusion and nonresponse, which are also the variables used in survey weighting and clustering. However, such models can quickly become very complicated, with potentially thousands of poststratification cells. It is then a challenge to develop general families of multilevel probability models that yield reasonable Bayesian inferences. We discuss in the context of several ongoing public health and social surveys. This work is currently open-ended, and we conclude with thoughts on how research could proceed to solve these problems.
“Struggles With Survey Weighting And Regression Modeling” Metadata:
- Title: ➤ Struggles With Survey Weighting And Regression Modeling
- Author: Andrew Gelman
- Language: English
Edition Identifiers:
- Internet Archive ID: arxiv-0710.5005
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 10.30 Mbs, the file-s for this book were downloaded 72 times, the file-s went public at Mon Sep 23 2013.
Available formats:
Abbyy GZ - Animated GIF - Archive BitTorrent - DjVu - DjVuTXT - Djvu XML - Item Tile - Metadata - Scandata - Single Page Processed JP2 ZIP - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Struggles With Survey Weighting And Regression Modeling at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
41An Investigation Of Multivariate Adaptive Regression Splines For Modeling And Analysis Of Univariate And Semi-multivariate Time Series Systems.
By Stevens, James G.;Lewis, Peter A. W.
Dissertation supervisor, P. Lewis
“An Investigation Of Multivariate Adaptive Regression Splines For Modeling And Analysis Of Univariate And Semi-multivariate Time Series Systems.” Metadata:
- Title: ➤ An Investigation Of Multivariate Adaptive Regression Splines For Modeling And Analysis Of Univariate And Semi-multivariate Time Series Systems.
- Author: ➤ Stevens, James G.;Lewis, Peter A. W.
- Language: en_US,eng
Edition Identifiers:
- Internet Archive ID: investigationofm00stevpdf
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 179.60 Mbs, the file-s for this book were downloaded 198 times, the file-s went public at Fri Oct 09 2015.
Available formats:
Abbyy GZ - Animated GIF - Archive BitTorrent - DjVu - DjVuTXT - Djvu XML - Item Tile - Metadata - Scandata - Single Page Processed JP2 ZIP - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find An Investigation Of Multivariate Adaptive Regression Splines For Modeling And Analysis Of Univariate And Semi-multivariate Time Series Systems. at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
42Modeling Credit Spreads Using Nonlinear Regression
By Radoslava Mirkov, Thomas Maul, Ronald Hochreiter and Holger Thomae
The term structure of credit spreads is studied with an aim to predict its future movements. A completely new approach to tackle this problem is presented, which utilizes nonlinear parametric models. The Brain-Cousens regression model with five parameters is chosen to describe the term structure of credit spreads. Further, we investigate the dependence of the parameter changes over time and the determinants of credit spreads.
“Modeling Credit Spreads Using Nonlinear Regression” Metadata:
- Title: ➤ Modeling Credit Spreads Using Nonlinear Regression
- Authors: Radoslava MirkovThomas MaulRonald HochreiterHolger Thomae
“Modeling Credit Spreads Using Nonlinear Regression” Subjects and Themes:
- Subjects: Quantitative Finance - Statistical Finance
Edition Identifiers:
- Internet Archive ID: arxiv-1401.6955
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 0.20 Mbs, the file-s for this book were downloaded 30 times, the file-s went public at Sat Jun 30 2018.
Available formats:
Archive BitTorrent - Metadata - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Modeling Credit Spreads Using Nonlinear Regression at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
43Identifying Rank-influential Groups Of Observations In Linear Regression Modeling
By Kempthorne, Peter J and Sloan School of Management
The term structure of credit spreads is studied with an aim to predict its future movements. A completely new approach to tackle this problem is presented, which utilizes nonlinear parametric models. The Brain-Cousens regression model with five parameters is chosen to describe the term structure of credit spreads. Further, we investigate the dependence of the parameter changes over time and the determinants of credit spreads.
“Identifying Rank-influential Groups Of Observations In Linear Regression Modeling” Metadata:
- Title: ➤ Identifying Rank-influential Groups Of Observations In Linear Regression Modeling
- Authors: Kempthorne, Peter JSloan School of Management
- Language: English
Edition Identifiers:
- Internet Archive ID: identifyingranki00kemp
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 47.02 Mbs, the file-s for this book were downloaded 640 times, the file-s went public at Thu Sep 25 2008.
Available formats:
Abbyy GZ - Animated GIF - Archive BitTorrent - Cloth Cover Detection Log - DjVu - DjVuTXT - Djvu XML - Dublin Core - Grayscale PDF - JPEG Thumb - MARC - MARC Binary - MARC Source - Metadata - OCR Page Index - OCR Search Text - Page Numbers JSON - Scan Factors - Scandata - Single Page Original JP2 Tar - Single Page Processed JP2 ZIP - Text PDF - chOCR - hOCR -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Identifying Rank-influential Groups Of Observations In Linear Regression Modeling at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
44DTIC ADA170904: Modeling And Interpreting Construction Production Data: A Regression Approach.
By Defense Technical Information Center
This thesis examines elevated concrete slab production data from a high-rise building. The importance of collecting pertinent information during construction is discussed. A nine step approach is presented as a methodology for analyzing construction data by linear regression. Examples are given to show how analysis results may be used in the evaluation of alternative construction methods and in deciding the number of workers to be assigned to a crew. (Author)
“DTIC ADA170904: Modeling And Interpreting Construction Production Data: A Regression Approach.” Metadata:
- Title: ➤ DTIC ADA170904: Modeling And Interpreting Construction Production Data: A Regression Approach.
- Author: ➤ Defense Technical Information Center
- Language: English
“DTIC ADA170904: Modeling And Interpreting Construction Production Data: A Regression Approach.” Subjects and Themes:
- Subjects: ➤ DTIC Archive - Bohne,Christopher B - AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH - *REGRESSION ANALYSIS - *CONSTRUCTION - *STRUCTURAL ENGINEERING - MATHEMATICAL MODELS - MANPOWER UTILIZATION - BUILDINGS - THESES - CONCRETE - VARIABLES - CREWS - DATA ACQUISITION - LABOR
Edition Identifiers:
- Internet Archive ID: DTIC_ADA170904
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 46.30 Mbs, the file-s for this book were downloaded 43 times, the file-s went public at Fri Feb 09 2018.
Available formats:
Abbyy GZ - Archive BitTorrent - DjVuTXT - Djvu XML - Item Tile - Metadata - OCR Page Index - OCR Search Text - Page Numbers JSON - Scandata - Single Page Processed JP2 ZIP - Text PDF - chOCR - hOCR -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find DTIC ADA170904: Modeling And Interpreting Construction Production Data: A Regression Approach. at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
45Variable Selection In Semiparametric Regression Modeling
By Runze Li and Hua Liang
In this paper, we are concerned with how to select significant variables in semiparametric modeling. Variable selection for semiparametric regression models consists of two components: model selection for nonparametric components and selection of significant variables for the parametric portion. Thus, semiparametric variable selection is much more challenging than parametric variable selection (e.g., linear and generalized linear models) because traditional variable selection procedures including stepwise regression and the best subset selection now require separate model selection for the nonparametric components for each submodel. This leads to a very heavy computational burden. In this paper, we propose a class of variable selection procedures for semiparametric regression models using nonconcave penalized likelihood. We establish the rate of convergence of the resulting estimate. With proper choices of penalty functions and regularization parameters, we show the asymptotic normality of the resulting estimate and further demonstrate that the proposed procedures perform as well as an oracle procedure. A semiparametric generalized likelihood ratio test is proposed to select significant variables in the nonparametric component. We investigate the asymptotic behavior of the proposed test and demonstrate that its limiting null distribution follows a chi-square distribution which is independent of the nuisance parameters. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed variable selection procedures.
“Variable Selection In Semiparametric Regression Modeling” Metadata:
- Title: ➤ Variable Selection In Semiparametric Regression Modeling
- Authors: Runze LiHua Liang
- Language: English
Edition Identifiers:
- Internet Archive ID: arxiv-0803.1931
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 11.36 Mbs, the file-s for this book were downloaded 79 times, the file-s went public at Wed Sep 18 2013.
Available formats:
Abbyy GZ - Animated GIF - Archive BitTorrent - DjVu - DjVuTXT - Djvu XML - Item Tile - Metadata - Scandata - Single Page Processed JP2 ZIP - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Variable Selection In Semiparametric Regression Modeling at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
46Comment: Struggles With Survey Weighting And Regression Modeling
By Roderick J. Little
Comment: Struggles with Survey Weighting and Regression Modeling [arXiv:0710.5005]
“Comment: Struggles With Survey Weighting And Regression Modeling” Metadata:
- Title: ➤ Comment: Struggles With Survey Weighting And Regression Modeling
- Author: Roderick J. Little
- Language: English
Edition Identifiers:
- Internet Archive ID: arxiv-0710.5013
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 3.21 Mbs, the file-s for this book were downloaded 73 times, the file-s went public at Mon Sep 23 2013.
Available formats:
Abbyy GZ - Animated GIF - Archive BitTorrent - DjVu - DjVuTXT - Djvu XML - Item Tile - Metadata - Scandata - Single Page Processed JP2 ZIP - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Comment: Struggles With Survey Weighting And Regression Modeling at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
47Predictor-dependent Shrinkage For Linear Regression Via Partial Factor Modeling
By P. Richard Hahn, Sayan Mukherjee and Carlos Carvalho
In prediction problems with more predictors than observations, it can sometimes be helpful to use a joint probability model, $\pi(Y,X)$, rather than a purely conditional model, $\pi(Y \mid X)$, where $Y$ is a scalar response variable and $X$ is a vector of predictors. This approach is motivated by the fact that in many situations the marginal predictor distribution $\pi(X)$ can provide useful information about the parameter values governing the conditional regression. However, under very mild misspecification, this marginal distribution can also lead conditional inferences astray. Here, we explore these ideas in the context of linear factor models, to understand how they play out in a familiar setting. The resulting Bayesian model performs well across a wide range of covariance structures, on real and simulated data.
“Predictor-dependent Shrinkage For Linear Regression Via Partial Factor Modeling” Metadata:
- Title: ➤ Predictor-dependent Shrinkage For Linear Regression Via Partial Factor Modeling
- Authors: P. Richard HahnSayan MukherjeeCarlos Carvalho
- Language: English
Edition Identifiers:
- Internet Archive ID: arxiv-1011.3725
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 9.51 Mbs, the file-s for this book were downloaded 52 times, the file-s went public at Sat Sep 21 2013.
Available formats:
Abbyy GZ - Animated GIF - Archive BitTorrent - DjVu - DjVuTXT - Djvu XML - Item Tile - Metadata - Scandata - Single Page Processed JP2 ZIP - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Predictor-dependent Shrinkage For Linear Regression Via Partial Factor Modeling at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
48Efficient Algorithm To Select Tuning Parameters In Sparse Regression Modeling With Regularization
By Kei Hirose, Shohei Tateishi and Sadanori Konishi
In sparse regression modeling via regularization such as the lasso, it is important to select appropriate values of tuning parameters including regularization parameters. The choice of tuning parameters can be viewed as a model selection and evaluation problem. Mallows' $C_p$ type criteria may be used as a tuning parameter selection tool in lasso-type regularization methods, for which the concept of degrees of freedom plays a key role. In the present paper, we propose an efficient algorithm that computes the degrees of freedom by extending the generalized path seeking algorithm. Our procedure allows us to construct model selection criteria for evaluating models estimated by regularization with a wide variety of convex and non-convex penalties. Monte Carlo simulations demonstrate that our methodology performs well in various situations. A real data example is also given to illustrate our procedure.
“Efficient Algorithm To Select Tuning Parameters In Sparse Regression Modeling With Regularization” Metadata:
- Title: ➤ Efficient Algorithm To Select Tuning Parameters In Sparse Regression Modeling With Regularization
- Authors: Kei HiroseShohei TateishiSadanori Konishi
- Language: English
Edition Identifiers:
- Internet Archive ID: arxiv-1109.2411
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 10.75 Mbs, the file-s for this book were downloaded 111 times, the file-s went public at Mon Sep 23 2013.
Available formats:
Abbyy GZ - Animated GIF - Archive BitTorrent - DjVu - DjVuTXT - Djvu XML - Item Tile - Metadata - Scandata - Single Page Processed JP2 ZIP - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find Efficient Algorithm To Select Tuning Parameters In Sparse Regression Modeling With Regularization at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
49DTIC ADA293771: Modeling F/A-18 Flight Hour Program Costs Using Regression Analysis.
By Defense Technical Information Center
This thesis is an in depth analysis of cost variance in Naval Air Reserve units flying the McDonnell Douglas F/A-18. The purpose of the thesis is to identify, analyze and quantify the effect of variances in the cost per flight hour of the Naval Air Reserve's Flying Hour Program. The study begins with a review of the Planning, Programming, and Budgeting System which is used to justify and fund the Flying Hour Program. Then three different methods of determining Flying Hour Program requirements are described. The four components of cost per hour within the Flying Hour Program (Fuel, Organizational Maintenance Activity, Intermediate Maintenance Activity and Aviation Depot Level Repairables) are defined. Finally, using regression analysis techniques, these four components of F/A-18 cost data are analyzed on the basis of the intensity of aircraft utilization: flight hours. The analysis includes a regression model to provide budgeters at the headquarter or squadron level the means for predicting aircraft maintenance and fuel costs given a utilization rate. The thesis concludes with areas recommended for further research. (AN)
“DTIC ADA293771: Modeling F/A-18 Flight Hour Program Costs Using Regression Analysis.” Metadata:
- Title: ➤ DTIC ADA293771: Modeling F/A-18 Flight Hour Program Costs Using Regression Analysis.
- Author: ➤ Defense Technical Information Center
- Language: English
“DTIC ADA293771: Modeling F/A-18 Flight Hour Program Costs Using Regression Analysis.” Subjects and Themes:
- Subjects: ➤ DTIC Archive - Arkley, Larry E. - NAVAL POSTGRADUATE SCHOOL MONTEREY CA - *MATHEMATICAL MODELS - *REGRESSION ANALYSIS - *JET FIGHTERS - *PLANNING PROGRAMMING BUDGETING - DATA BASES - MILITARY RESERVES - MILITARY REQUIREMENTS - NAVAL AVIATION - MANAGEMENT PLANNING AND CONTROL - COST EFFECTIVENESS - AIRCRAFT MAINTENANCE - FLIGHT TRAINING - COST ANALYSIS - COMBAT READINESS - ANALYSIS OF VARIANCE - THESES - SUPPLY DEPOTS - NAVAL AIRCRAFT - FUEL CONSUMPTION - NAVAL OPERATIONS - NAVAL BUDGETS - SQUAD LEVEL ORGANIZATIONS.
Edition Identifiers:
- Internet Archive ID: DTIC_ADA293771
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 136.50 Mbs, the file-s for this book were downloaded 81 times, the file-s went public at Thu Mar 22 2018.
Available formats:
Abbyy GZ - Additional Text PDF - Archive BitTorrent - DjVuTXT - Djvu XML - Image Container PDF - JPEG Thumb - Metadata - OCR Page Index - OCR Search Text - Page Numbers JSON - Scandata - Single Page Processed JP2 ZIP - chOCR - hOCR -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find DTIC ADA293771: Modeling F/A-18 Flight Hour Program Costs Using Regression Analysis. at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
50ERIC EJ1086512: Modeling Student Performance In Mathematics Using Binary Logistic Regression At Selected Secondary Schools A Case Study Of Mtwara Municipality And Ilemela District
By ERIC
This study investigated the performance of secondary school students in Mathematics at the Selected Secondary Schools in Mtwara Municipality and Ilemela District by Absenteeism, Conduct, Type of School and Gender as explanatory Factors. The data used in the study was collected from documented records of 250 form three students with 1:1 gender ratio--50 students from each of the five selected secondary schools in the academic year 2011/2012. The sample was considered appropriate as they had covered more than half of the Mathematics syllabus in Ordinary Secondary Schools. Binary logistic regression was used to model a binary variable "performance" (fail, pass) against a systematic component of linear combination predictors (absenteeism, conduct, type of school and gender). The model fitted for the log-odds in favour of poor performance is log[subscript "e"](??(?) divided by 1 - ??(?) = -1.185 + 0.346 ?[subscript 1] + 1.137 ?[subscript 2]. The essence of this study is to provide student performance analysis method (Binary Logistic Regression) not commonly used in Tanzania. Findings show that two out of four explanatory factors used in the study (absenteeism and misconduct) significantly predict student performance in Mathematics based on binary logistic regression fitted. Absenteeism and misconduct predict the log-odds of poor performance by multiplicative effect of 1.414 and 3.137 respectively. Future work is recommended to focus on analysis using other Generalized Linear Models (GLM) as well considering other locations with more/other variables affecting performance of students in mathematics.
“ERIC EJ1086512: Modeling Student Performance In Mathematics Using Binary Logistic Regression At Selected Secondary Schools A Case Study Of Mtwara Municipality And Ilemela District” Metadata:
- Title: ➤ ERIC EJ1086512: Modeling Student Performance In Mathematics Using Binary Logistic Regression At Selected Secondary Schools A Case Study Of Mtwara Municipality And Ilemela District
- Author: ERIC
- Language: English
“ERIC EJ1086512: Modeling Student Performance In Mathematics Using Binary Logistic Regression At Selected Secondary Schools A Case Study Of Mtwara Municipality And Ilemela District” Subjects and Themes:
- Subjects: ➤ ERIC Archive - Foreign Countries - Mathematics Instruction - Secondary School Mathematics - Secondary School Students - Attendance - Student Behavior - Institutional Characteristics - Gender Differences - Student Records - Regression (Statistics) - Mathematics Achievement - Predictor Variables - Mabula, Salyungu
Edition Identifiers:
- Internet Archive ID: ERIC_EJ1086512
Downloads Information:
The book is available for download in "texts" format, the size of the file-s is: 5.67 Mbs, the file-s for this book were downloaded 47 times, the file-s went public at Wed Oct 03 2018.
Available formats:
Abbyy GZ - Archive BitTorrent - DjVuTXT - Djvu XML - Item Tile - Metadata - Scandata - Single Page Processed JP2 ZIP - Text PDF -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
Online Marketplaces
Find ERIC EJ1086512: Modeling Student Performance In Mathematics Using Binary Logistic Regression At Selected Secondary Schools A Case Study Of Mtwara Municipality And Ilemela District at online marketplaces:
- Amazon: Audiable, Kindle and printed editions.
- Ebay: New & used books.
Buy “Regression Modeling” online:
Shop for “Regression Modeling” on popular online marketplaces.
- Ebay: New and used books.