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1Probability And Statistical Inference For Engineers; A First Course

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144 p. 20 cm

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2ERIC EJ910174: Using Stimulus Equivalence Technology To Teach Statistical Inference In A Group Setting

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Computerized lessons employing stimulus equivalence technology, used previously under laboratory conditions to teach inferential statistics concepts to college students, were employed in a group setting for the first time. Students showed the same directly taught and emergent learning gains as in laboratory studies. A brief paper-and-pencil examination, suitable for classroom use, captured effects demonstrated previously through laboratory tests. The results support the extension of the lessons to more naturalistic settings. (Contains 1 table and 2 figures.)

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3Journal Of Statistical Planning And Inference 1995: Vol 46 Index

Journal of Statistical Planning and Inference 1995: Volume 46 , Issue Index. Digitized from IA1653324-06 . Previous issue: sim_journal-of-statistical-planning-and-inference_1995-06_45_3 . Next issue: sim_journal-of-statistical-planning-and-inference_1995-07_46_1 .

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4Journal Of Statistical Planning And Inference 1998: Vol 69 Index

Journal of Statistical Planning and Inference 1998: Volume 69 , Issue Index. Digitized from IA1653324-07 . Previous issue: sim_journal-of-statistical-planning-and-inference_1998-05-15_68_2 . Next issue: sim_journal-of-statistical-planning-and-inference_1998-06-01_69_1 .

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5Journal Of Statistical Planning And Inference 2001: Vol 97 Index

Journal of Statistical Planning and Inference 2001: Volume 97 , Issue Index. Digitized from IA1652430-01 . Previous issue: sim_journal-of-statistical-planning-and-inference_2001-07-01_96_2_0 . Next issue: sim_journal-of-statistical-planning-and-inference_2001-08-01_97_1_0 .

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6Journal Of Statistical Planning And Inference 2002: Vol 106 Index

Journal of Statistical Planning and Inference 2002: Volume 106 , Issue Index. Digitized from IA1652430-01 . Previous issue: sim_journal-of-statistical-planning-and-inference_2002-07-01_105_2_0 . Next issue: sim_journal-of-statistical-planning-and-inference_2002_106_contents_0 .

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7Journal Of Statistical Planning And Inference 2003: Vol 113 Index

Journal of Statistical Planning and Inference 2003: Volume 113 , Issue Index. Digitized from IA1652430-01 . Previous issue: sim_journal-of-statistical-planning-and-inference_2003-03-01_112_1-2_0 . Next issue: sim_journal-of-statistical-planning-and-inference_2003-04-01_113_1_0 .

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8Journal Of Statistical Planning And Inference 2003: Vol 115 Index

Journal of Statistical Planning and Inference 2003: Volume 115 , Issue Index. Digitized from IA1653325-03 . Previous issue: sim_journal-of-statistical-planning-and-inference_2003-06-01_114_1-2 . Next issue: sim_journal-of-statistical-planning-and-inference_2003-07-01_115_1 .

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9Journal Of Statistical Planning And Inference 1999: Vol 78 Table Of Contents

Journal of Statistical Planning and Inference 1999: Volume 78 , Issue Contents. Digitized from IA1652430-07 . Previous issue: sim_journal-of-statistical-planning-and-inference_1999_78_index_0 . Next issue: sim_journal-of-statistical-planning-and-inference_1999-05-01_78_1-2_0 .

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10Journal Of Statistical Planning And Inference 1999: Vol 78 Index

Journal of Statistical Planning and Inference 1999: Volume 78 , Issue Index. Digitized from IA1652430-07 . Previous issue: sim_journal-of-statistical-planning-and-inference_1999-03-01_77_2_0 . Next issue: sim_journal-of-statistical-planning-and-inference_1999_78_contents_0 .

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11Journal Of Statistical Planning And Inference 2001: Vol 94 Index

Journal of Statistical Planning and Inference 2001: Volume 94 , Issue Index. Digitized from IA1653325-03 . Previous issue: sim_journal-of-statistical-planning-and-inference_2001-02-01_93_1-2 . Next issue: sim_journal-of-statistical-planning-and-inference_2001-03-01_94_1 .

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12Linear Statistical Inference : Proceedings Of The International Conference Held At Poznań, Poland, June 4-8, 1984

Journal of Statistical Planning and Inference 2001: Volume 94 , Issue Index. Digitized from IA1653325-03 . Previous issue: sim_journal-of-statistical-planning-and-inference_2001-02-01_93_1-2 . Next issue: sim_journal-of-statistical-planning-and-inference_2001-03-01_94_1 .

“Linear Statistical Inference : Proceedings Of The International Conference Held At Poznań, Poland, June 4-8, 1984” Metadata:

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13Statistical Model For Overdispersed Count Outcome With Many Zeros: An Approach For Direct Marginal Inference

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Marginalized models are in great demand by most researchers in the life sciences particularly in clinical trials, epidemiology, health-economics, surveys and many others since they allow generalization of inference to the entire population under study. For count data, standard procedures such as the Poisson regression and negative binomial model provide population average inference for model parameters. However, occurrence of excess zero counts and lack of independence in empirical data have necessitated their extension to accommodate these phenomena. These extensions, though useful, complicates interpretations of effects. For example, the zero-inflated Poisson model accounts for the presence of excess zeros but the parameter estimates do not have a direct marginal inferential ability as its base model, the Poisson model. Marginalizations due to the presence of excess zeros are underdeveloped though demand for such is interestingly high. The aim of this paper is to develop a marginalized model for zero-inflated univariate count outcome in the presence of overdispersion. Emphasis is placed on methodological development, efficient estimation of model parameters, implementation and application to two empirical studies. A simulation study is performed to assess the performance of the model. Results from the analysis of two case studies indicated that the refined procedure performs significantly better than models which do not simultaneously correct for overdispersion and presence of excess zero counts in terms of likelihood comparisons and AIC values. The simulation studies also supported these findings. In addition, the proposed technique yielded small biases and mean square errors for model parameters. To ensure that the proposed method enjoys widespread use, it is implemented using the SAS NLMIXED procedure with minimal coding efforts.

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14Spatio-temporal Ornstein-Uhlenbeck Processes: Theory, Simulation And Statistical Inference

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Spatio-temporal modelling is an increasingly popular topic in Statistics. Our paper contributes to this line of research by developing the theory, simulation and inference for a spatio-temporal Ornstein-Uhlenbeck process. We conduct detailed simulation studies and demonstrate the practical relevance of these processes in an empirical study of radiation anomaly data. Finally, we describe how predictions can be carried out in the Gaussian setting.

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15Stochastic Modeling And Statistical Inference Of Intrinsic Noise In Gene Regulation System Via Chemical Master Equation

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Intrinsic noise, the stochastic cell-to-cell fluctuations in mRNAs and proteins, has been observed and proved to play important roles in cellular systems. Due to the recent development in single-cell-level measurement technology, the studies on intrinsic noise are becoming increasingly popular among scholars. The chemical master equation (CME) has been used to model the evolutions of complex chemical and biological systems since 1940, and are often served as the standard tool for modeling intrinsic noise in gene regulation system. A CME-based model can capture the discrete, stochastic, and dynamical nature of gene regulation system, and may offer casual and physical explanation of the observed data at single-cell level. Nonetheless, the complexity of CME also pose serious challenge for researchers in proposing practical modeling and inference frameworks. In this article, we will review the existing works on the modelings and inference of intrinsic noise in gene regulation system within the framework of CME model. We will explore the principles in constructing a CME model for studying gene regulation system and discuss the popular approximations of CME. Then we will study the simulation simulation methods as well as the analytical and numerical approaches that can be used to obtain solution to a CME model. Finally we will summary the exiting statistical methods that can be used to infer the unknown parameters or structures in CME model using single-cell-level gene expression data.

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16ERIC ED029511: Computer-Based Instruction In Statistical Inference; Final Report. Technical Memorandum (TM Series).

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A two-year investigation into the development of computer-assisted instruction (CAI) for the improvement of undergraduate training in statistics was undertaken. The first year was largely devoted to designing PLANIT (Programming LANguage for Interactive Teaching) which reduces, or completely eliminates, the need an author of CAI lessons would otherwise have for professional programing assistance. Four lessons were constructed with PLANIT and presented in the second year to 21 undergraduates on two California campuses. Each student received four to five hours of instruction including PLANIT communication procedures and conventions, elementary probability, descriptive statistics, and computer programing. It was concluded that CAI is best employed in the numerical demonstration of statistical concepts and for statistical laboratory exercise instruction. Now, and for some years to come, only the more routine aspects of tutorial assistance can be provided with CAI; however, this assistance should be beneficial to both students and instructors. Documentation is provided. (GO)

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17Probability And Statistical Inference

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These lectures introduce key concepts in probability and statistical inference at a level suitable for graduate students in particle physics. Our goal is to paint as vivid a picture as possible of the concepts covered.

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18Statistical Inference For Time-inhomogeneous Volatility Models

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This paper offers a new approach for estimating and forecasting the volatility of financial time series. No assumption is made about the parametric form of the processes. On the contrary, we only suppose that the volatility can be approximated by a constant over some interval. In such a framework, the main problem consists of filtering this interval of time homogeneity; then the estimate of the volatility can be simply obtained by local averaging. We construct a locally adaptive volatility estimate (LAVE) which can perform this task and investigate it both from the theoretical point of view and through Monte Carlo simulations. Finally, the LAVE procedure is applied to a data set of nine exchange rates and a comparison with a standard GARCH model is also provided. Both models appear to be capable of explaining many of the features of the data; nevertheless, the new approach seems to be superior to the GARCH method as far as the out-of-sample results are concerned.

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19Journal Of Statistical Planning And Inference 2003: Vol 112 Table Of Contents

Journal of Statistical Planning and Inference 2003: Volume 112 , Issue CONTENTS. Digitized from IA1653325-03 . Previous issue: sim_journal-of-statistical-planning-and-inference_2003_112_index . Next issue: sim_journal-of-statistical-planning-and-inference_2003-03-01_112_1-2 .

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20Statistical Inference; The Distribution-free Approach

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Journal of Statistical Planning and Inference 2003: Volume 112 , Issue CONTENTS. Digitized from IA1653325-03 . Previous issue: sim_journal-of-statistical-planning-and-inference_2003_112_index . Next issue: sim_journal-of-statistical-planning-and-inference_2003-03-01_112_1-2 .

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21NASA Technical Reports Server (NTRS) 20060051869: Exploring The Connection Between Sampling Problems In Bayesian Inference And Statistical Mechanics

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The Bayesian and statistical mechanical communities often share the same objective in their work - estimating and integrating probability distribution functions (pdfs) describing stochastic systems, models or processes. Frequently, these pdfs are complex functions of random variables exhibiting multiple, well separated local minima. Conventional strategies for sampling such pdfs are inefficient, sometimes leading to an apparent non-ergodic behavior. Several recently developed techniques for handling this problem have been successfully applied in statistical mechanics. In the multicanonical and Wang-Landau Monte Carlo (MC) methods, the correct pdfs are recovered from uniform sampling of the parameter space by iteratively establishing proper weighting factors connecting these distributions. Trivial generalizations allow for sampling from any chosen pdf. The closely related transition matrix method relies on estimating transition probabilities between different states. All these methods proved to generate estimates of pdfs with high statistical accuracy. In another MC technique, parallel tempering, several random walks, each corresponding to a different value of a parameter (e.g. "temperature"), are generated and occasionally exchanged using the Metropolis criterion. This method can be considered as a statistically correct version of simulated annealing. An alternative approach is to represent the set of independent variables as a Hamiltonian system. Considerab!e progress has been made in understanding how to ensure that the system obeys the equipartition theorem or, equivalently, that coupling between the variables is correctly described. Then a host of techniques developed for dynamical systems can be used. Among them, probably the most powerful is the Adaptive Biasing Force method, in which thermodynamic integration and biased sampling are combined to yield very efficient estimates of pdfs. The third class of methods deals with transitions between states described by rate constants. These problems are isomorphic with chemical kinetics problems. Recently, several efficient techniques for this purpose have been developed based on the approach originally proposed by Gillespie. Although the utility of the techniques mentioned above for Bayesian problems has not been determined, further research along these lines is warranted

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22NPTEL Lectures: Mathematics - Statistical Inference

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Lectures from the National Programme on Technology Enhanced Learning - Mathematics - Statistical Inference

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23DTIC ADA480995: Actively Learning Specific Function Properties With Applications To Statistical Inference

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Active learning techniques have previously been shown to be extremely effective for learning a target function over an entire parameter space based on a limited set of observations. However, in many cases, only a specific property of the target function needs to be learned. For instance, when discovering the boundary of a region such as the locations in which the wireless network strength is above some operable level, we are interested in learning only the level-set of the target function. While techniques that learn the entire target function over the parameter space can be used to detection specific properties of the target function (e.g. level-sets), methods that learn only the required properties can be significantly more efficient, especially as the dimensionality of the parameter space increases. These active learning algorithms have a natural application in many statistical inference techniques. For example, given a set of data and a physical model of the data, which is a function of several variables, a scientist is often interested in determining the ranges of the variables which are statistically supported by the data. We show that many frequentist statistical inference techniques can be reduced to a level-set detection problem or similar search of a property of the target function , and hence benefit from active learning algorithms which target specific properties. Using these active learning algorithms significantly decreases the number of experiments required to accurately detect the boundaries of the desired 1 confidence regions. Moreover, since computing the model of the data given the input parameters may be expensive (either computationally, or monetarily), such algorithms can facilitate analyses that were previously infeasible. We demonstrate the use of several statistical inference techniques combined with active learning algorithms on several cosmological data sets.

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24DTIC ADA536910: Cost-Performance Tradeoff In Multi-hop Aggregation For Statistical Inference

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The problem of distributed fusion for binary hypothesis testing in a multihop network is considered. The sensor measurements are spatially correlated according to a Markov random field (MRF) under both the hypotheses. A fusion scheme for detection involves selection and localized processing of a subset of sensor measurements, fusion of these processed values to form a sufficient statistic, and its delivery to the fusion center. The goal is to find a fusion scheme that achieves optimal linear tradeoff between the total routing costs and the resulting detection error exponent at the fusion center. The Neyman-Pearson error exponent, under a fixed type-I bound, is shown to be the limit of the normalized sum of the Kullback-Leibler distances (KLD) over the maximal cliques of the MRF under some convergence conditions. It is shown that optimal fusion reduces to a prize-collecting Steiner tree (PCST) with the approximation factor preserved when the cliques of the MRF are disjoint. The PCST is found over an expanded communication graph with virtual nodes added for each non-trivial maximal clique of the MRF and their KLD assigned as the node penalty.

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25DTIC AD1013915: Investigation Of Statistical Inference Methodologies Through Scale Model Propagation Experiments

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The long-term scientific goal of this project is to advance the understanding of statistical inference methodologies for ocean-acoustic problems by investigating and applying statistical methods to data collected from scale-model propagation experiments.

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26ERIC ED462419: Bayesian Statistical Inference For Coefficient Alpha. ACT Research Report Series.

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Coefficient alpha is a simple and very useful index of test reliability that is widely used in educational and psychological measurement. Classical statistical inference for coefficient alpha is well developed. This paper presents two methods for Bayesian statistical inference for a single sample alpha coefficient. An approximate analytic method based on conjugate distributions is derived. This method is easy to compute. A second method uses Markov Chain Monte Carlo (MCMC) methodology as implemented by the computer program WinBUGS. WinBUGS may be downloaded for free from the Internet, and this paper includes WinBUGS code for making Bayesian inferences about coefficient alpha. Pseudo-randomly generated data are used to compare the two Bayesian methods to each other and both of these methods to the classical method. The results indicate that the two Bayesian methods work well as long as the number of items and examinees are not too small. Appendixes discuss the derivation of the marginal likelihood, contain WinBUGS code, and present the figures. (Contains 1 table, 9 figures, and 11 references.) (Author/SLD)

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27Statistical Inference Optimized With Respect To The Observed Sample For Single Or Multiple Comparisons

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The normalized maximum likelihood (NML) is a recent penalized likelihood that has properties that justify defining the amount of discrimination information (DI) in the data supporting an alternative hypothesis over a null hypothesis as the logarithm of an NML ratio, namely, the alternative hypothesis NML divided by the null hypothesis NML. The resulting DI, like the Bayes factor but unlike the p-value, measures the strength of evidence for an alternative hypothesis over a null hypothesis such that the probability of misleading evidence vanishes asymptotically under weak regularity conditions and such that evidence can support a simple null hypothesis. Unlike the Bayes factor, the DI does not require a prior distribution and is minimax optimal in a sense that does not involve averaging over outcomes that did not occur. Replacing a (possibly pseudo-) likelihood function with its weighted counterpart extends the scope of the DI to models for which the unweighted NML is undefined. The likelihood weights leverage side information, either in data associated with comparisons other than the comparison at hand or in the parameter value of a simple null hypothesis. Two case studies, one involving multiple populations and the other involving multiple biological features, indicate that the DI is robust to the type of side information used when that information is assigned the weight of a single observation. Such robustness suggests that very little adjustment for multiple comparisons is warranted if the sample size is at least moderate.

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28Statistical Inference On Panel Data Models: A Kernel Ridge Regression Method

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We propose statistical inferential procedures for panel data models with interactive fixed effects in a kernel ridge regression framework.Compared with traditional sieve methods, our method is automatic in the sense that it does not require the choice of basis functions and truncation parameters.Model complexity is controlled by a continuous regularization parameter which can be automatically selected by generalized cross validation. Based on empirical processes theory and functional analysis tools, we derive joint asymptotic distributions for the estimators in the heterogeneous setting. These joint asymptotic results are then used to construct confidence intervals for the regression means and prediction intervals for the future observations, both being the first provably valid intervals in literature. Marginal asymptotic normality of the functional estimators in homogeneous setting is also obtained. Simulation and real data analysis demonstrate the advantages of our method.

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29P7.1 Of Statistical Inference At Coursera - Chuck Vs Bruce

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Comparison of Chuck Norris vs Bruce Lee performances in movies

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30Statistical Inference

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Comparison of Chuck Norris vs Bruce Lee performances in movies

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31Some Basic Theory For Statistical Inference

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Comparison of Chuck Norris vs Bruce Lee performances in movies

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32Journal Of Statistical Planning And Inference 2001: Vol 98 Index

Journal of Statistical Planning and Inference 2001: Volume 98 , Issue Index. Digitized from IA1652430-01 . Previous issue: sim_journal-of-statistical-planning-and-inference_2001-09-01_97_2_0 . Next issue: sim_journal-of-statistical-planning-and-inference_2001_98_contents_0 .

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33Journal Of Statistical Planning And Inference 1988: Vol 20 Index

Journal of Statistical Planning and Inference 1988: Volume 20 , Issue Index. Digitized from IA1653324-05 . Previous issue: sim_journal-of-statistical-planning-and-inference_1988-07_19_3 . Next issue: sim_journal-of-statistical-planning-and-inference_1988-09_20_1 .

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34Journal Of Statistical Planning And Inference 1990: Vol 25 Index

Journal of Statistical Planning and Inference 1990: Volume 25 , Issue Index. Digitized from IA1653324-05 . Previous issue: sim_journal-of-statistical-planning-and-inference_1990-03_24_3 . Next issue: sim_journal-of-statistical-planning-and-inference_1990-05_25_1 .

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35Journal Of Statistical Planning And Inference 1990: Vol 25 Index

Journal of Statistical Planning and Inference 1990: Volume 25 , Issue Index. Digitized from IA1652430-07 . Previous issue: sim_journal-of-statistical-planning-and-inference_1990-03_24_3_0 . Next issue: sim_journal-of-statistical-planning-and-inference_1990-05_25_1_0 .

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36Measurement Model For Statistical Inference Completeness Indication

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Establishes the factor structure for a measurement model on coded completeness indicators on statistical inferences of research studies.

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37DTIC ADA622177: Statistical Inference For Detecting Structures And Anomalies In Networks

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Work under this grant focused on methods for extracting hidden information from network data, including data from social networks, networks of communications and interactions, heath or disease networks, and brain networks. During the last 12 months of this project, the funding level was cut substantially. Nevertheless, over this period, our team worked on several substantial projects, including the development of several powerful new algorithms for analyzing networks and their application to specific real-world domains. These efforts produced 8 peer-reviewed papers or new preprints, and more than a dozen invited or contributed presentations on these projects. We continued to focus on developing powerful and scalable Bayesian statistical and related inference methods for community structure, hierarchies, core-periphery structure, rankings, and other large-scale network structures, and on discovering the fundamental limits of these techniques for inferring such hidden patterns. Additionally, we focused on algorithms applicable to very large networks, networks with auxiliary information (such as annotations, temporal dynamics, or edge weights), and demonstrations of these techniques to domains of interest.

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38DTIC AD1028617: Statistical Inference In Factor Analysis

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In this paper we discuss some methods of factor analysis. The entire discussion is centeredaround one general probability model. We consider some mathematical problemsof the model, such as whether certain kinds of observed data determine the model uniquely.We treat the statistical problems of estimation and tests of certain hypotheses. Forthese purposes the asymptotic distribution theory of some statistics is treated.The primary aim of this paper is to give a unified exposition of this part of factor analysisfrom the viewpoint of the mathematical statistician. The literature on factor analysisis scattered; moreover, the many papers and books have been written from many differentpoints of view. By confining ourselves to one model and by emphasizing statisticalinferences for this model we hope to present a clear picture to the statistician.The development given here is expected to point up features of model-building andstatistical inference that occur in other areas where statistical theories are being developed.For example, nearly all of the problems met in factor analysis are met in latentstructure analysis.

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39Introduction To Data Analysis And Statistical Inference

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In this paper we discuss some methods of factor analysis. The entire discussion is centeredaround one general probability model. We consider some mathematical problemsof the model, such as whether certain kinds of observed data determine the model uniquely.We treat the statistical problems of estimation and tests of certain hypotheses. Forthese purposes the asymptotic distribution theory of some statistics is treated.The primary aim of this paper is to give a unified exposition of this part of factor analysisfrom the viewpoint of the mathematical statistician. The literature on factor analysisis scattered; moreover, the many papers and books have been written from many differentpoints of view. By confining ourselves to one model and by emphasizing statisticalinferences for this model we hope to present a clear picture to the statistician.The development given here is expected to point up features of model-building andstatistical inference that occur in other areas where statistical theories are being developed.For example, nearly all of the problems met in factor analysis are met in latentstructure analysis.

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40DTIC ADA218473: Bayesian Nonparametric Prediction And Statistical Inference

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The problem of Bayesian nonparametric prediction and statistical inference is formulated and discussed. A solution is proposed based upon A sub n and H sub n as in Hill (1968). The meaning of parameters in the subjective Bayesian theory of Bruno de Finetti is discussed in connection both with A sub n and with conventional parametric models. It is argued that the usual sharp distinction between prediction and parametric inference is largely illusory. The finite version of de Finetti's theorem is emphasized for the practice of statistics, with the infinite case used only to obtain approximations and insight. (kr)

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41Intro To Statistical Inference - Assignment 7

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The problem of Bayesian nonparametric prediction and statistical inference is formulated and discussed. A solution is proposed based upon A sub n and H sub n as in Hill (1968). The meaning of parameters in the subjective Bayesian theory of Bruno de Finetti is discussed in connection both with A sub n and with conventional parametric models. It is argued that the usual sharp distinction between prediction and parametric inference is largely illusory. The finite version of de Finetti's theorem is emphasized for the practice of statistics, with the infinite case used only to obtain approximations and insight. (kr)

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42Distributed Statistical Inference In Networks Of Social Interaction

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This experiment studies how groups aggregate information in a distributed inference task across different communication modalities.

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43Basic Concepts In Statistical Inference

This experiment studies how groups aggregate information in a distributed inference task across different communication modalities.

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44Journal Of Statistical Planning And Inference 1998: Vol 74 Index

Journal of Statistical Planning and Inference 1998: Volume 74 , Issue Index. Digitized from IA1652430-07 . Previous issue: sim_journal-of-statistical-planning-and-inference_1998-09-01_73_1-2_0 . Next issue: sim_journal-of-statistical-planning-and-inference_1998-10-01_74_1_0 .

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45Journal Of Statistical Planning And Inference 1995: Vol 43 Index

Journal of Statistical Planning and Inference 1995: Volume 43 , Issue Index. Digitized from IA1653324-06 . Previous issue: sim_journal-of-statistical-planning-and-inference_1994-12_42_3 . Next issue: sim_journal-of-statistical-planning-and-inference_1995-01_43_1-2 .

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46The Foundations Of Statistical Inference;

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112 p. 19 cm

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47Journal Of Statistical Planning And Inference 1996: Vol 56 Index

Journal of Statistical Planning and Inference 1996: Volume 56 , Issue Index. Digitized from IA1653324-06 . Previous issue: sim_journal-of-statistical-planning-and-inference_1996-11-08_55_3 . Next issue: sim_journal-of-statistical-planning-and-inference_1996-12-01_56_1 .

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48Journal Of Statistical Planning And Inference 2002: Vol 104 Index

Journal of Statistical Planning and Inference 2002: Volume 104 , Issue Index. Digitized from IA1653325-03 . Previous issue: sim_journal-of-statistical-planning-and-inference_2002-04-15_103_1-2 . Next issue: sim_journal-of-statistical-planning-and-inference_2002-05-01_104_1 .

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49Journal Of Statistical Planning And Inference 1989: Vol 21 Index

Journal of Statistical Planning and Inference 1989: Volume 21 , Issue Index. Digitized from IA1652430-07 . Previous issue: sim_journal-of-statistical-planning-and-inference_1988-11_20_3_0 . Next issue: sim_journal-of-statistical-planning-and-inference_1989-01_21_1_0 .

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50Graphical Inference For Statistical Tests

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Central to confirmatory data analysis is the concept of determining whether two data samples are drawn from the same or different populations. For the simplest of such inferential statistical tests---the Z family of tests---this essentially amounts to fitting a normal distribution to each sample and then determining the overlap between them. However, even Z-tests are not understandable to the general population, and this is doubly true for more sophisticated statistical tests. Here we will study how graphical formulations of such Z-tests can support casual users with no specialized statistical training in assessing the differences between two or more data samples.

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