Downloads and Free Reading Options

The purpose of this study is to investigate how survey respondents classify and rank different data points in different public health data streams. Survey respondents are shown two interactive html files with 5 streams each and are asked first to classify the one highlighted point in each stream as concerning or not concerning. They then rank points that they found concerning.

There are no author-identified significant results in this report.

We propose an inlier-based outlier detection method capable of both identifying the outliers and explaining why they are outliers, by identifying the outlier-specific features. Specifically, we employ an inlier-based outlier detection criterion, which uses the ratio of inlier and test probability densities as a measure of plausibility of being an outlier. For estimating the density ratio function, we propose a localized logistic regression algorithm. Thanks to the locality of the model, variable selection can be outlier-specific, and will help interpret why points are outliers in a high-dimensional space. Through synthetic experiments, we show that the proposed algorithm can successfully detect the important features for outliers. Moreover, we show that the proposed algorithm tends to outperform existing algorithms in benchmark datasets.

We propose an inlier-based outlier detection method capable of both identifying the outliers and explaining why they are outliers, by identifying the outlier-specific features. Specifically, we employ an inlier-based outlier detection criterion, which uses the ratio of inlier and test probability densities as a measure of plausibility of being an outlier. For estimating the density ratio function, we propose a localized logistic regression algorithm. Thanks to the locality of the model, variable selection can be outlier-specific, and will help interpret why points are outliers in a high-dimensional space. Through synthetic experiments, we show that the proposed algorithm can successfully detect the important features for outliers. Moreover, we show that the proposed algorithm tends to outperform existing algorithms in benchmark datasets.

Malcolm Gladwell shows why the story of success is far more surprising, and more fascinating, than we could ever imagine Why do some people achieve so much more than others? Can they lie so far out of the ordinary? In his provocative and inspiring book, Malcolm Gladwell looks at everyone from rock stars to professional athletes, software billionaires to scientific geniuses, to show that the story of success is far more surprising, and more fascinating, than we could ever have imagined. He reveals that it's as much about where we're from and what we do, as who we are - and that no one, not even a genius, ever makes it alone. Outliers will change the way you think about your own life story, and about what makes us all unique. Like Blink, this is a landmark work that will simultaneously delight and illuminate.

Malcolm Gladwell shows why the story of success is far more surprising, and more fascinating, than we could ever imagine Why do some people achieve so much more than others? Can they lie so far out of the ordinary? In his provocative and inspiring book, Malcolm Gladwell looks at everyone from rock stars to professional athletes, software billionaires to scientific geniuses, to show that the story of success is far more surprising, and more fascinating, than we could ever have imagined. He reveals that it's as much about where we're from and what we do, as who we are - and that no one, not even a genius, ever makes it alone. Outliers will change the way you think about your own life story, and about what makes us all unique. Like Blink, this is a landmark work that will simultaneously delight and illuminate.

Journal of Research of the National Bureau of Standards

Difference in outliers of Independent and Major Studio Productions

This brief summarizes successful strategies for creating a strong and stable teaching force. These strategies are drawn from "positive outlier" districts in California that have excelled at helping African American, Latino/a, and White students achieve at high levels on assessments of academic standards in English language arts and mathematics. Case studies of seven of these districts indicate several effective strategies for recruiting, developing, and retaining high-quality teachers. These strategies include a clear philosophy and effective process for teacher hiring, a well-developed teacher pipeline, a strategic long-term commitment to professional growth, and a focus on teacher retention. [This brief is based on the full report, "Closing the Opportunity Gap: How Positive Outlier Districts in California Are Pursuing Equitable Access to Deeper Learning. Positive Outliers Series" (ED610874).]

This article suggests multivariate kurtosis measure as a statistic for detection of outliers in a multivariate linear regression model. The statistics has some local optimal properties. Keywords: Multivariate linear regression model; Detection of outliers; Multivariate kurtosis; and Locally best invariant test.

For an island right on the equator with plenty of sunlight, Singapore has a very small number of solar-powered homes. This is mainly due to the lack of space in high-rise apartment blocks and the high cost of solar panels. Dr Lau, owner of a solar-powered home, and Mr Christophe Inglin, a solar panel expert, weigh in on the costs and benefits of installing solar panels in Singapore. About the Series: In statistics, an outlier refers to a person whose abilities, achievements, etc., lie outside the range of statistical probability. "Outliers" the series profiles inspiring Singaporeans who follow unbeaten paths, and deviate from societal norms.

We present in this paper a new tool for outliers detection in the context of multiple regression models. This graphical tool is based on recursive estimation of the parameters. Simulations were carried out to illustrate the performance of this graphical procedure. As a conclusion, this tool is applied to real data containing outliers according to the classical available tools.

Odds are that you’ve been hearing more and more about cryptocurrency as digital tokens like bitcoin and ethereum have become valuable commodities. Converts (and investors) say that cryptocurrencies built on blockchain technology represent the future of money, finance, and commerce. But skeptics say that digital currencies represent crowd-sourced pyramid schemes or are fuel for another tech bubble. We met with Olaf Carlson-Wee, who was the first employee at the cryptocurrency broker Coinbase, where he famously took his entire salary in bitcoin. Now, Carlson-Wee runs a hedge fund that deals exclusively in crypto-assets. We talked with Carlson-Wee in San Francisco about money, trust, and how he made his friends rich. Subscribe to VICE News here: [URL REMOVED] Check out VICE News for more: [URL REMOVED] Follow VICE News here: Facebook: [URL REMOVED] Twitter: [URL REMOVED] Tumblr: [URL REMOVED] Instagram: [URL REMOVED] More videos from the VICE network: [URL REMOVED]

An application of moving-horizon strategy for nonlinear systems with possible outliers in measurements is addressed. With the increased success of movinghorizon strategy in the state estimation for linear systems with outliers acting on the measurement, investigating the nonlinear approach is highly required. In this paper we applied the nonlinear version which has been presented in the literature in term of discrete-time linear time-invariant systems, where the applied strategy considers minimizing a least-squares functions in which each measure possibly contaminated by outlier is left out in turn and the lowest cost is propagated. The moving horizon filter effectiveness as compared with the extended Kalman filter is shown by means of simulation example and estimation error comparison. The moving horizon filter shows the feature of resisting outliers with robust estimation

An application of moving-horizon strategy for nonlinear systems with possible outliers in measurements is addressed. With the increased success of movinghorizon strategy in the state estimation for linear systems with outliers acting on the measurement, investigating the nonlinear approach is highly required. In this paper we applied the nonlinear version which has been presented in the literature in term of discrete-time linear time-invariant systems, where the applied strategy considers minimizing a least-squares functions in which each measure possibly contaminated by outlier is left out in turn and the lowest cost is propagated. The moving horizon filter effectiveness as compared with the extended Kalman filter is shown by means of simulation example and estimation error comparison. The moving horizon filter shows the feature of resisting outliers with robust estimation

Fraud Detection is of great importance to financial institutions. In this paper we have tried to study the Outlier Analysis in Stock Market Fraud Detection. Outlier Analysis is a fundamental issue in Data Mining, specifically in Fraud Detection. While observing the Indian Stock Market, we could detect that some of the Trading Entities have suspicious trading patterns that give rise to a doubt of having some malpractices in stock transactions within Indian Stock Market. All the facts are presented on the basis of data obtained from the official sites of BSE (Bombay Stock Exchange), NSE (National Stock Exchange) and SEBI (Securities and Exchange Board of India).

We derive the joint asymptotic distribution of the outlier eigenvalues of an additively deformed Wigner matrix. Our only assumptions on the deformation are that its rank be fixed and its norm bounded.

We are trying to give a mathematically correct definition of outliers. Our approach is based on the distance between two last order statistics and appears to be connected to the law of large numbers. Key words: outliers, law of large numbers, heavy tails, stability index.

This article is from BioMedical Engineering OnLine , volume 11 . Abstract Background: Undetected arrhythmic beats seriously affect the power spectrum of the heart rate variability (HRV). Therefore, the series of RR intervals are normally carefully edited before HRV is analysed, but this is a time consuming procedure when 24-hours recordings are analysed. Alternatively, different methods can be used for automatic removal of arrhythmic beats and artefacts. This study compared common frequency domain indices of HRV when determined from manually edited and automatically filtered RR intervals. Methods and Results: Twenty-four hours Holter recordings were available from 140 healthy subjects of age 1-75 years. An experienced technician carefully edited all recordings. Automatic filtering was performed using a recursive procedure where RR intervals were removed if they differed from the mean of the surrounding RR intervals with more than a predetermined limit (ranging from 10% to 50%). The filtering algorithm was evaluated by replacing 1% of the beats with synthesised ectopic beats. Power spectral analysis was performed before and after filtering of both the original edited data and the noisy data set. The results from the analysis using the noisy data were used to define an age-based filtering threshold. The age-based filtration was evaluated with completely unedited data, generated by removing all annotations from the series of RR intervals, and then comparing the resulting HRV indices with those obtained using edited data. The results showed equivalent results after age-based filtration of both the edited and unedited data sets, where the differences in HRV indices obtained by different preprocessing methods were small compared to the mean values within each age group. Conclusions: The study showed that it might not be necessary to perform the time-consuming careful editing of all detected heartbeats before HRV is analysed in Holter recordings.In most subjects, it is sufficient to perform the regular editing needed for valid arrhythmia analyses, and then remove undetected ectopic beats and artefacts by age-based filtration of the series of RR intervals, particularly in subjects older than 30 years.

Long Beach Unified School District (LBUSD) has been nationally recognized as a consistently high-functioning district for more than 2 decades. The district educates approximately 72,200 students, from preschool to high school, in its 86 schools. Almost 90% are students of color, with 57% Latino/a and 12% African American, while 65% are from economically disadvantaged families and 15% are English learners. LBUSD is one of seven districts studied by researchers at the Learning Policy Institute in a mixed-methods study that sought to learn from positive outlier districts in which African American, Latino/a, and White students did better than predicted on California's math and English language arts tests from 2015 through 2017, after accounting for differences in socioeconomic status. This in-depth case study describes the critical practices and policies within LBUSD that have promoted student learning, especially among students of color, in the context of the Common Core State Standards (CCSS) and the deeper learning they seek to foster. Through an analysis of interview, documentary, and observational evidence, this case study describes key factors that enabled student success in LBUSD.

Malcolm Gladwell

Using methods of statistical mechanics, we analyse the effect of outliers on the supervised learning of a classification problem. The learning strategy aims at selecting informative examples and discarding outliers. We compare two algorithms which perform the selection either in a soft or a hard way. When the fraction of outliers grows large, the estimation errors undergo a first order phase transition.

Using methods of statistical mechanics, we analyse the effect of outliers on the supervised learning of a classification problem. The learning strategy aims at selecting informative examples and discarding outliers. We compare two algorithms which perform the selection either in a soft or a hard way. When the fraction of outliers grows large, the estimation errors undergo a first order phase transition.

We call attention against what seems to a widely held misconception according to which large crashes are the largest events of distributions of price variations with fat tails. We demonstrate on the Dow Jones Industrial index that with high probability the three largest crashes in this century are outliers. This result supports suggestion that large crashes result from specific amplification processes that might lead to observable pre-cursory signatures.

Kuesioner yang akan dibagikan mencakup pernyataan-pernyataan yang relevan dengan kriteria yang telah ditetapkan untuk penelitian ini. Jika ditemukan peserta yang tidak memenuhi kriteria dalam penelitian ini, maka data mereka tidak akan dimasukkan ke dalam perhitungan uji analisis regresi linear sederhana (simple linear regression). Selain itu, distribusi responden akan diuji menggunakan diagram untuk memeriksa apakah data terdistribusi secara normal. Outlier kemudian akan diabaikan dan tidak dimasukkan dalam uji regresi.

When the Signal-to-Noise Ratio (SNR) falls below a certain level, the error of the Time-of-Flight (ToF) Maximum Likelihood Estimator (MLE) increases abruptly due to the well known threshold effect. Nevertheless, operating near and below the threshold SNR value might be necessary for many remote sensing applications due to power-related constraints. These constrains may include a limit on the maximum power of a single source pulse or a limit on the total power used by multiple signals transmitted during a single measurement. For narrowband signals, the threshold effect emerges mostly due to outliers induced by local maxima of the autocorrelation function of a source signal. Following the previously explored path of biosonar-inspired echo processing, in this research we introduce new methods for ToF estimation in the presence of outliers. The proposed methods employ a bank of phase-shifted unmatched filters for generating multiple biased but only partially correlated estimators (multiple experts). Using machine-learning techniques, the information from the multiple experts is combined together for improving the near-the-threshold ToF estimation from a single echo. We describe methods for ToF estimation from single and multiple pulses as well as the method for improving the energy efficiency of the estimation.

This item represents a case in PACER, the U.S. Government's website for federal case data. If you wish to see the entire case, please consult PACER directly.

The Box and Cox (1964) power transformation family and robust alternatives developed by Bickel and Doksum (1981) and Carroll (1980) are applied to data sets given by John (1978). The robust methods perform quite well compared to normal theory likelihood methods. (Author)

WOO... do we go for it in this episode!!?!  ?? This is a "cross training" episode where we discuss on thoughts and takeaways from Malcolm Gladwell's book, Outliers.  AND WE GET INTENSE... and slightly LONG winded.  If you make it to the end, drop a ????for us on instagram. NEXT CROSS TRAINING BOOK:   I am Malala by Malala Yousafzai Finishers: Mandy: Justified - Originally on FX, now on Hulu Haley: Apashe - DJ - this links you to a 30 minute long mix, but he has plenty of other stuff to check out 

Many problems in bioinformatics are about finding strings that approximately represent a collection of given strings. We look at more general problems where some input strings can be classified as outliers. The Close to Most Strings problem is, given a set S of same-length strings, and a parameter d, find a string x that maximizes the number of "non-outliers" within Hamming distance d of x. We prove this problem has no PTAS unless ZPP=NP, correcting a decade-old mistake. The Most Strings with Few Bad Columns problem is to find a maximum-size subset of input strings so that the number of non-identical positions is at most k; we show it has no PTAS unless P=NP. We also observe Closest to k Strings has no EPTAS unless W[1]=FPT. In sum, outliers help model problems associated with using biological data, but we show the problem of finding an approximate solution is computationally difficult.

While most Singaporeans spend hours in public transport, a small number is starting to cycle to work despite inconveniences like traffic congestion and a lack of bike racks. One cyclist believes he can get to work quicker by bike than by public transport. About the Series: In statistics, an outlier refers to a person whose abilities, achievements, etc., lie outside the range of statistical probability. "Outliers" the series profiles inspiring Singaporeans who follow unbeaten paths, and deviate from societal norms.

Image: "Plot of Outliers Identified in Bayesian Analysis " , published by Open Context at: http://opencontext.org/media/9d73c515-6cbd-4e0b-9c9f-d8942ed0f3d4 ; as part of the " Architecture and Urbanism at Seyitömer Höyük, Turkey " data publication.

This paper describes a new filtering technique used to detect and eliminate outlying data in two-way satellite time and frequency transfer (TWSTFT) time links. In the case of TWSTFT data used to calculate International Atomic Time (TAI), three main problems have to be considered: 1. the difficulty of recognizing outliers from useful data; 2. the need to avoid deleting useful data; 3. that TWSTFT links can show an underlying slope which renders the standard treatment more difficult. Using phase and frequency filtering techniques, a new way of detecting outliers, while avoiding detection of useful data, has been developed and implemented at the BIPM to clean TWSTFT data.

This paper describes a new filtering technique used to detect and eliminate outlying data in two-way satellite time and frequency transfer (TWSTFT) time links. In the case of TWSTFT data used to calculate International Atomic Time (TAI), three main problems have to be considered: 1. the difficulty of recognizing outliers from useful data; 2. the need to avoid deleting useful data; 3. that TWSTFT links can show an underlying slope which renders the standard treatment more difficult. Using phase and frequency filtering techniques, a new way of detecting outliers, while avoiding detection of useful data, has been developed and implemented at the BIPM to clean TWSTFT data.

Sequoia Capital, a venture capital pioneer, has backed iconic startups like Apple & Google, investing in outliers to drive innovation and long-term growth.

Located just southeast of Fresno in California's Central Valley, Sanger Unified School District (USD) serves approximately 12,000 students in 20 schools. Sanger USD students are predominately from low-income families (73%); most are Latino/a (70%), and about one in five (18%) are English learners. During the accountability era of No Child Left Behind, Sanger had earned the reputation of being a turnaround district based on district students' steep and steady improvement on California's Test of Basic Skills between 2004 and 2012. Its success hinged on developing an organizational culture of continuous improvement and an instructional regime of direct instruction for students' basic skills mastery. This case study addresses the question of how Sanger USD managed to shift instruction and student support to achieve exemplary results on the new state assessments of students' deeper learning. Sanger USD is one of seven districts studied by researchers at the Learning Policy Institute (LPI) in a mixed-methods study that sought to learn from positive outlier districts in which African American, Latino/a, and White students did better than predicted on California's math and English language arts tests from 2015 through 2017, after accounting for differences in socioeconomic status. This in-depth case study describes the critical practices and policies within Sanger USD that have promoted student learning, especially among students of color, in the context of the Common Core State Standards (CCSS) and the deeper learning they seek to foster. Through analyses of interviews, observations, and documents--and building upon the authors' prior research on Sanger's turnaround--this case study describes key factors that enabled district students to outperform their peers on tests of deeper learning.

Located just southeast of Fresno in California's Central Valley, Sanger Unified School District (USD) serves approximately 12,000 students in 20 schools. Sanger USD students are predominately from low-income families (73%); most are Latino/a (70%), and about one in five (18%) are English learners. During the accountability era of No Child Left Behind, Sanger had earned the reputation of being a turnaround district based on district students' steep and steady improvement on California's Test of Basic Skills between 2004 and 2012. Its success hinged on developing an organizational culture of continuous improvement and an instructional regime of direct instruction for students' basic skills mastery. This case study addresses the question of how Sanger USD managed to shift instruction and student support to achieve exemplary results on the new state assessments of students' deeper learning. Sanger USD is one of seven districts studied by researchers at the Learning Policy Institute (LPI) in a mixed-methods study that sought to learn from positive outlier districts in which African American, Latino/a, and White students did better than predicted on California's math and English language arts tests from 2015 through 2017, after accounting for differences in socioeconomic status. This in-depth case study describes the critical practices and policies within Sanger USD that have promoted student learning, especially among students of color, in the context of the Common Core State Standards (CCSS) and the deeper learning they seek to foster. Through analyses of interviews, observations, and documents--and building upon the authors' prior research on Sanger's turnaround--this case study describes key factors that enabled district students to outperform their peers on tests of deeper learning.

Mavericks Of Religion: The Irish Outliers Of Southern Literature

Several regression methods were examined within the framework of weighted structural regression (WSR), comparing their regression weight stability and score estimation accuracy in the presence of outlier contamination. The methods compared are: (1) ordinary least squares; (2) WSR ridge regression; (3) minimum risk regression; (4) minimum risk 2; (5) goodness of fit index (GFI); and (6) WSR reduced rank regression. Three population covariance matrices were used that were drawn from applied behavioral science literature as the basis for generating samples, some of which were contaminated. A bootstrap method was used to compare the regression methods. Analysis resulted in 4 sets of bootstrap samples for each of the population systems, 12 in all. Results support the notion of increased efficacy of the adaptive forms of WSR in small sample applications where outlier contamination exists. The improvement over conventional least squares is not always substantial, but it is notable that adaptive forms of WSR based on the concept of empirical Bayes covariance estimation can provide consistent and sometimes substantial improvement over conventional methods. Five tables present analysis data. Appendix A provides the basis for the WSR class of methods. Appendices B and C each contain three tables of analysis information. (SLD)

Customized version of http://www.thingiverse.com/thing:1159436 Created with Customizer! http://www.thingiverse.com/apps/customizer/run?thing_id=1159436

This paper considers the problem of matrix completion when the observed entries are noisy and contain outliers. It begins with introducing a new optimization criterion for which the recovered matrix is defined as its solution. This criterion uses the celebrated Huber function from the robust statistics literature to downweigh the effects of outliers. A practical algorithm is developed to solve the optimization involved. This algorithm is fast, straightforward to implement, and monotonic convergent. Furthermore, the proposed methodology is theoretically shown to be stable in a well defined sense. Its promising empirical performance is demonstrated via a sequence of simulation experiments, including image inpainting.

Extreme events have an important role which is sometime catastrophic in a variety of natural phenomena including climate, earthquakes and turbulence, as well as in man-made environments like financial markets. Statistical analysis and predictions in such systems are complicated by the fact that on the one hand extreme events may appear as "outliers" whose statistical properties do not seem to conform with the bulk of the data, and on the other hands they dominate the (fat) tails of probability distributions and the scaling of high moments, leading to "abnormal" or "multi"-scaling. We employ a shell model of turbulence to show that it is very useful to examine in detail the dynamics of onset and demise of extreme events. Doing so may reveal dynamical scaling properties of the extreme events that are characteristic to them, and not shared by the bulk of the fluctuations. As the extreme events dominate the tails of the distribution functions, knowledge of their dynamical scaling properties can be turned into a prediction of the functional form of the tails. We show that from the analysis of relatively short time horizons (in which the extreme events appear as outliers) we can predict the tails of the probability distribution functions, in agreement with data collected in very much longer time horizons. The conclusion is that events that may appear unpredictable on relatively short time horizons are actually a consistent part of a multiscaling statistics on longer time horizons.

Serving a working-class suburb of the Los Angeles metropolitan area, the Hawthorne School District (HSD) is a small district with 11 schools: seven elementary schools, three middle schools, and one dependent charter high school. HSD's students are predominantly from low-income families (87%). Most students (71%) are Latino/a and about fifth (21%) are African American, with smaller numbers of Asian, Filipino, Pacific Islander, and White students. HSD is one of seven districts studied by researchers at the Learning Policy Institute in a mixed-methods study, which sought to learn from positive outlier districts in which African American, Latino/a, and White students did better than predicted on California's math and English language arts tests from 2015 through 2017, after accounting for differences in socioeconomic status. This in-depth case study complements the research series by describing the critical practices and policies within HSD that have promoted student learning, especially among students of color, in the context of the Common Core State Standards (CCSS) and the deeper learning they seek to foster. Through an analysis of interviews and documents, this case study describes key factors that participants described as central to the district's success.

本教程我们将向您演示EViews 12中的指标饱和度，该演示可以自动检测和建模异常值以及最小二乘回归中常数和趋势的变化。 完整的视频教程，请登录 科学软件网 www.sciencesoftware.com.cn 观看。

本教程我们将向您演示EViews 12中的指标饱和度，该演示可以自动检测和建模异常值以及最小二乘回归中常数和趋势的变化。 完整的视频教程，请登录 科学软件网 www.sciencesoftware.com.cn 观看。

There’s much to digest from this interview with Jay Naidoo, a now 68-year-old who grew up as the last-born of eight children - and whose life was overturned when Apartheid’s Group Areas Act collided with the location of a modest family home. The social engineering system switched him from medical student to trade unionist; but it was intellect and passion that propelled the iconic activist into the top rank of anti-Apartheid warriors. In this moving, reflective discussion, Naidoo explains why he still refuses to accept the obvious, and continues to publicly pose thorny questions in a personal quest to leave the world a better place. He spoke to Alec Hogg of BizNews.

Do you know of anyone who can finish 10 full meals under 30 minutes? Watch how fitness trainer, Zermatt Neo, trains to keep fit and eat competitively at the same time. About the series: In statistics, an outlier refers to a person whose abilities, achievements, etc., lie outside the range of statistical probability. "Outliers" the series profiles inspiring Singaporeans who follow unbeaten paths, and deviate from societal norms.

Ler Lee Cheng has been making her own detergent for the last four years in an effort to lead a more organic lifestyle. Check out how she makes floor cleaner, laundry detergent and more from fruit peels and soap nuts. About the Series: In statistics, an outlier refers to a person whose abilities, achievements, etc., lie outside the range of statistical probability. "Outliers" the series profiles inspiring Singaporeans who follow unbeaten paths, and deviate from societal norms.

Customized version of https://www.thingiverse.com/thing:3559949 Created with Customizer! https://www.thingiverse.com/apps/customizer/run?thing_id=3559949

She may be petite, but she works with structures much bigger than her modest frame. Loy Ju Lin is an architect conservationist who spent the last 7 years giving new life to old buildings. What challenges does she face as a conservationist in Singapore? How does she find that balance between old and new? About the Series: In statistics, an outlier refers to a person whose abilities, achievements, etc., lie outside the range of statistical probability. "Outliers" the series profiles inspiring Singaporeans who follow unbeaten paths, and deviate from societal norms.