Robust Statistical Methods With R Second Edition

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Robust Statistics

Ricardo A. Maronna,R. Douglas Martin,Victor J. Yohai,Matías Salibián-Barrera
Robust Statistics Book PDF

Author : Ricardo A. Maronna,R. Douglas Martin,Victor J. Yohai,Matías Salibián-Barrera
Publisher : John Wiley & Sons
Page : 466 pages
File Size : 43,8 Mb
Release : 2019-01-04
Category : Mathematics
ISBN : 9781119214687

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Robust Statistics by Ricardo A. Maronna,R. Douglas Martin,Victor J. Yohai,Matías Salibián-Barrera Pdf

A new edition of this popular text on robust statistics, thoroughly updated to include new and improved methods and focus on implementation of methodology using the increasingly popular open-source software R. Classical statistics fail to cope well with outliers associated with deviations from standard distributions. Robust statistical methods take into account these deviations when estimating the parameters of parametric models, thus increasing the reliability of fitted models and associated inference. This new, second edition of Robust Statistics: Theory and Methods (with R) presents a broad coverage of the theory of robust statistics that is integrated with computing methods and applications. Updated to include important new research results of the last decade and focus on the use of the popular software package R, it features in-depth coverage of the key methodology, including regression, multivariate analysis, and time series modeling. The book is illustrated throughout by a range of examples and applications that are supported by a companion website featuring data sets and R code that allow the reader to reproduce the examples given in the book. Unlike other books on the market, Robust Statistics: Theory and Methods (with R) offers the most comprehensive, definitive, and up-to-date treatment of the subject. It features chapters on estimating location and scale; measuring robustness; linear regression with fixed and with random predictors; multivariate analysis; generalized linear models; time series; numerical algorithms; and asymptotic theory of M-estimates. Explains both the use and theoretical justification of robust methods Guides readers in selecting and using the most appropriate robust methods for their problems Features computational algorithms for the core methods Robust statistics research results of the last decade included in this 2nd edition include: fast deterministic robust regression, finite-sample robustness, robust regularized regression, robust location and scatter estimation with missing data, robust estimation with independent outliers in variables, and robust mixed linear models. Robust Statistics aims to stimulate the use of robust methods as a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. It is an ideal resource for researchers, practitioners, and graduate students in statistics, engineering, computer science, and physical and social sciences.

Robust Statistical Methods with R, Second Edition

Jana Jurečková,Jan Picek,Martin Schindler
Robust Statistical Methods with R Second Edition Book PDF

Author : Jana Jurečková,Jan Picek,Martin Schindler
Publisher : CRC Press
Page : 208 pages
File Size : 49,8 Mb
Release : 2019-05-29
Category : Mathematics
ISBN : 9781351975124

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Robust Statistical Methods with R, Second Edition by Jana Jurečková,Jan Picek,Martin Schindler Pdf

The second edition of Robust Statistical Methods with R provides a systematic treatment of robust procedures with an emphasis on new developments and on the computational aspects. There are many numerical examples and notes on the R environment, and the updated chapter on the multivariate model contains additional material on visualization of multivariate data in R. A new chapter on robust procedures in measurement error models concentrates mainly on the rank procedures, less sensitive to errors than other procedures. This book will be an invaluable resource for researchers and postgraduate students in statistics and mathematics. Features • Provides a systematic, practical treatment of robust statistical methods • Offers a rigorous treatment of the whole range of robust methods, including the sequential versions of estimators, their moment convergence, and compares their asymptotic and finite-sample behavior • The extended account of multivariate models includes the admissibility, shrinkage effects and unbiasedness of two-sample tests • Illustrates the small sensitivity of the rank procedures in the measurement error model • Emphasizes the computational aspects, supplies many examples and illustrations, and provides the own procedures of the authors in the R software on the book’s website

Robust Statistical Methods with R, Second Edition

Jana Jurečková,Jan Picek,Martin Schindler
Robust Statistical Methods with R Second Edition Book PDF

Author : Jana Jurečková,Jan Picek,Martin Schindler
Publisher : CRC Press
Page : 254 pages
File Size : 42,5 Mb
Release : 2019-05-29
Category : Mathematics
ISBN : 9781351975131

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Robust Statistical Methods with R, Second Edition by Jana Jurečková,Jan Picek,Martin Schindler Pdf

The second edition of Robust Statistical Methods with R provides a systematic treatment of robust procedures with an emphasis on new developments and on the computational aspects. There are many numerical examples and notes on the R environment, and the updated chapter on the multivariate model contains additional material on visualization of multivariate data in R. A new chapter on robust procedures in measurement error models concentrates mainly on the rank procedures, less sensitive to errors than other procedures. This book will be an invaluable resource for researchers and postgraduate students in statistics and mathematics. Features • Provides a systematic, practical treatment of robust statistical methods • Offers a rigorous treatment of the whole range of robust methods, including the sequential versions of estimators, their moment convergence, and compares their asymptotic and finite-sample behavior • The extended account of multivariate models includes the admissibility, shrinkage effects and unbiasedness of two-sample tests • Illustrates the small sensitivity of the rank procedures in the measurement error model • Emphasizes the computational aspects, supplies many examples and illustrations, and provides the own procedures of the authors in the R software on the book’s website

Robust Statistical Methods with R

Jana Jureckova,Jan Picek
Robust Statistical Methods with R Book PDF

Author : Jana Jureckova,Jan Picek
Publisher : CRC Press
Page : 210 pages
File Size : 47,5 Mb
Release : 2005-11-29
Category : Mathematics
ISBN : 9781420035131

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Robust Statistical Methods with R by Jana Jureckova,Jan Picek Pdf

Robust statistical methods were developed to supplement the classical procedures when the data violate classical assumptions. They are ideally suited to applied research across a broad spectrum of study, yet most books on the subject are narrowly focused, overly theoretical, or simply outdated. Robust Statistical Methods with R provides a systemati

Robust Nonparametric Statistical Methods

Thomas P. Hettmansperger,Joseph W. McKean
Robust Nonparametric Statistical Methods Book PDF

Author : Thomas P. Hettmansperger,Joseph W. McKean
Publisher : John Wiley & Sons
Page : 492 pages
File Size : 50,8 Mb
Release : 1998
Category : Nonparametric statistics
ISBN : STANFORD:36105023161156

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Robust Nonparametric Statistical Methods by Thomas P. Hettmansperger,Joseph W. McKean Pdf

Offering an alternative to traditional statistical procedures which are based on least squares fitting, the authors cover such topics as one and two sample location models, linear models, and multivariate models. Both theory and applications are examined.

Fundamentals of Modern Statistical Methods

Rand R. Wilcox
Fundamentals of Modern Statistical Methods Book PDF

Author : Rand R. Wilcox
Publisher : Springer Science & Business Media
Page : 255 pages
File Size : 52,7 Mb
Release : 2010-03-18
Category : Social Science
ISBN : 9781441955241

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Fundamentals of Modern Statistical Methods by Rand R. Wilcox Pdf

Conventional statistical methods have a very serious flaw. They routinely miss differences among groups or associations among variables that are detected by more modern techniques, even under very small departures from normality. Hundreds of journal articles have described the reasons standard techniques can be unsatisfactory, but simple, intuitive explanations are generally unavailable. Situations arise where even highly nonsignificant results become significant when analyzed with more modern methods. Without assuming the reader has any prior training in statistics, Part I of this book describes basic statistical principles from a point of view that makes their shortcomings intuitive and easy to understand. The emphasis is on verbal and graphical descriptions of concepts. Part II describes modern methods that address the problems covered in Part I. Using data from actual studies, many examples are included to illustrate the practical problems with conventional procedures and how more modern methods can make a substantial difference in the conclusions reached in many areas of statistical research. The second edition of this book includes a number of advances and insights that have occurred since the first edition appeared. Included are new results relevant to medians, regression, measures of association, strategies for comparing dependent groups, methods for dealing with heteroscedasticity, and measures of effect size.

Introduction to Robust Estimation and Hypothesis Testing

Rand R. Wilcox
Introduction to Robust Estimation and Hypothesis Testing Book PDF

Author : Rand R. Wilcox
Publisher : Academic Press
Page : 610 pages
File Size : 45,5 Mb
Release : 2005-01-05
Category : Mathematics
ISBN : 9780127515427

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Introduction to Robust Estimation and Hypothesis Testing by Rand R. Wilcox Pdf

This revised book provides a thorough explanation of the foundation of robust methods, incorporating the latest updates on R and S-Plus, robust ANOVA (Analysis of Variance) and regression. It guides advanced students and other professionals through the basic strategies used for developing practical solutions to problems, and provides a brief background on the foundations of modern methods, placing the new methods in historical context. Author Rand Wilcox includes chapter exercises and many real-world examples that illustrate how various methods perform in different situations. Introduction to Robust Estimation and Hypothesis Testing, Second Edition, focuses on the practical applications of modern, robust methods which can greatly enhance our chances of detecting true differences among groups and true associations among variables. * Covers latest developments in robust regression * Covers latest improvements in ANOVA * Includes newest rank-based methods * Describes and illustrated easy to use software

Introduction to Robust Estimation and Hypothesis Testing

Rand R. Wilcox
Introduction to Robust Estimation and Hypothesis Testing Book PDF

Author : Rand R. Wilcox
Publisher : Academic Press
Page : 713 pages
File Size : 41,8 Mb
Release : 2012-01-12
Category : Mathematics
ISBN : 9780123869838

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Introduction to Robust Estimation and Hypothesis Testing by Rand R. Wilcox Pdf

"This book focuses on the practical aspects of modern and robust statistical methods. The increased accuracy and power of modern methods, versus conventional approaches to the analysis of variance (ANOVA) and regression, is remarkable. Through a combination of theoretical developments, improved and more flexible statistical methods, and the power of the computer, it is now possible to address problems with standard methods that seemed insurmountable only a few years ago"--

Robust Methods in Biostatistics

Stephane Heritier,Eva Cantoni,Samuel Copt,Maria-Pia Victoria-Feser
Robust Methods in Biostatistics Book PDF

Author : Stephane Heritier,Eva Cantoni,Samuel Copt,Maria-Pia Victoria-Feser
Publisher : John Wiley & Sons
Page : 292 pages
File Size : 51,6 Mb
Release : 2009-05-11
Category : Medical
ISBN : 047074054X

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Robust Methods in Biostatistics by Stephane Heritier,Eva Cantoni,Samuel Copt,Maria-Pia Victoria-Feser Pdf

Robust statistics is an extension of classical statistics that specifically takes into account the concept that the underlying models used to describe data are only approximate. Its basic philosophy is to produce statistical procedures which are stable when the data do not exactly match the postulated models as it is the case for example with outliers. Robust Methods in Biostatistics proposes robust alternatives to common methods used in statistics in general and in biostatistics in particular and illustrates their use on many biomedical datasets. The methods introduced include robust estimation, testing, model selection, model check and diagnostics. They are developed for the following general classes of models: Linear regression Generalized linear models Linear mixed models Marginal longitudinal data models Cox survival analysis model The methods are introduced both at a theoretical and applied level within the framework of each general class of models, with a particular emphasis put on practical data analysis. This book is of particular use for research students,applied statisticians and practitioners in the health field interested in more stable statistical techniques. An accompanying website provides R code for computing all of the methods described, as well as for analyzing all the datasets used in the book.

A Guide to Robust Statistical Methods

Rand R. Wilcox
A Guide to Robust Statistical Methods Book PDF

Author : Rand R. Wilcox
Publisher : Springer Nature
Page : 335 pages
File Size : 40,6 Mb
Release : 2023-11-26
Category : Mathematics
ISBN : 9783031417139

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A Guide to Robust Statistical Methods by Rand R. Wilcox Pdf

Robust statistical methods are now being used in a wide range of disciplines. The appeal of these methods is that they are designed to perform about as well as classic techniques when standard assumptions are true—but they continue to perform well in situations where classic methods perform poorly. This book provides a relatively non-technical guide to modern methods. The focus is on applying modern methods using R, understanding when and why classic methods can be unsatisfactory, and fostering a conceptual understanding of the relative merits of different techniques. A recurring theme is that no single method reveals everything one would like to know about the population under study. An appeal of robust methods is that under general conditions they provide much higher power than conventional techniques. Perhaps more importantly, they help provide a deeper and more nuanced understanding of data. The book is for readers who had at least one semester of statistics, aimed at non-statisticians.

Robust Nonparametric Statistical Methods

Thomas P. Hettmansperger,Joseph W. McKean
Robust Nonparametric Statistical Methods Book PDF

Author : Thomas P. Hettmansperger,Joseph W. McKean
Publisher : CRC Press
Page : 554 pages
File Size : 51,5 Mb
Release : 2010-12-20
Category : Mathematics
ISBN : 9781439809099

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Robust Nonparametric Statistical Methods by Thomas P. Hettmansperger,Joseph W. McKean Pdf

Presenting an extensive set of tools and methods for data analysis, Robust Nonparametric Statistical Methods, Second Edition covers univariate tests and estimates with extensions to linear models, multivariate models, times series models, experimental designs, and mixed models. It follows the approach of the first edition by developing rank-based m

Applied Compositional Data Analysis

Peter Filzmoser,Karel Hron,Matthias Templ
Applied Compositional Data Analysis Book PDF

Author : Peter Filzmoser,Karel Hron,Matthias Templ
Publisher : Springer
Page : 280 pages
File Size : 46,8 Mb
Release : 2018-11-03
Category : Mathematics
ISBN : 9783319964225

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Applied Compositional Data Analysis by Peter Filzmoser,Karel Hron,Matthias Templ Pdf

This book presents the statistical analysis of compositional data using the log-ratio approach. It includes a wide range of classical and robust statistical methods adapted for compositional data analysis, such as supervised and unsupervised methods like PCA, correlation analysis, classification and regression. In addition, it considers special data structures like high-dimensional compositions and compositional tables. The methodology introduced is also frequently compared to methods which ignore the specific nature of compositional data. It focuses on practical aspects of compositional data analysis rather than on detailed theoretical derivations, thus issues like graphical visualization and preprocessing (treatment of missing values, zeros, outliers and similar artifacts) form an important part of the book. Since it is primarily intended for researchers and students from applied fields like geochemistry, chemometrics, biology and natural sciences, economics, and social sciences, all the proposed methods are accompanied by worked-out examples in R using the package robCompositions.

Robust Statistics

Ricardo A. Maronna,Douglas R. Martin,Victor J. Yohai
Robust Statistics Book PDF

Author : Ricardo A. Maronna,Douglas R. Martin,Victor J. Yohai
Publisher : Wiley
Page : 436 pages
File Size : 46,5 Mb
Release : 2006-05-12
Category : Mathematics
ISBN : 0470010924

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Robust Statistics by Ricardo A. Maronna,Douglas R. Martin,Victor J. Yohai Pdf

Classical statistical techniques fail to cope well with deviations from a standard distribution. Robust statistical methods take into account these deviations while estimating the parameters of parametric models, thus increasing the accuracy of the inference. Research into robust methods is flourishing, with new methods being developed and different applications considered. Robust Statistics sets out to explain the use of robust methods and their theoretical justification. It provides an up-to-date overview of the theory and practical application of the robust statistical methods in regression, multivariate analysis, generalized linear models and time series. This unique book: Enables the reader to select and use the most appropriate robust method for their particular statistical model. Features computational algorithms for the core methods. Covers regression methods for data mining applications. Includes examples with real data and applications using the S-Plus robust statistics library. Describes the theoretical and operational aspects of robust methods separately, so the reader can choose to focus on one or the other. Supported by a supplementary website featuring time-limited S-Plus download, along with datasets and S-Plus code to allow the reader to reproduce the examples given in the book. Robust Statistics aims to stimulate the use of robust methods as a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. It is ideal for researchers, practitioners and graduate students of statistics, electrical, chemical and biochemical engineering, and computer vision. There is also much to benefit researchers from other sciences, such as biotechnology, who need to use robust statistical methods in their work.

R Primer, Second Edition

Claus Thorn Ekstrom
R Primer Second Edition Book PDF

Author : Claus Thorn Ekstrom
Publisher : CRC Press
Page : 427 pages
File Size : 41,5 Mb
Release : 2017-02-24
Category : Mathematics
ISBN : 9781351803731

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R Primer, Second Edition by Claus Thorn Ekstrom Pdf

Newcomers to R are often intimidated by the command-line interface, the vast number of functions and packages, or the processes of importing data and performing a simple statistical analysis. The R Primer provides a collection of concise examples and solutions to R problems frequently encountered by new users of this statistical software. This new edition adds coverage of R Studio and reproducible research.

The R Book

Michael J. Crawley
The R Book Book PDF

Author : Michael J. Crawley
Publisher : John Wiley & Sons
Page : 953 pages
File Size : 44,8 Mb
Release : 2007-06-13
Category : Mathematics
ISBN : 0470515066

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The R Book by Michael J. Crawley Pdf

The high-level language of R is recognized as one of the mostpowerful and flexible statistical software environments, and israpidly becoming the standard setting for quantitative analysis,statistics and graphics. R provides free access to unrivalledcoverage and cutting-edge applications, enabling the user to applynumerous statistical methods ranging from simple regression to timeseries or multivariate analysis. Building on the success of the author’s bestsellingStatistics: An Introduction using R, The R Book ispacked with worked examples, providing an all inclusive guide to R,ideal for novice and more accomplished users alike. The bookassumes no background in statistics or computing and introduces theadvantages of the R environment, detailing its applications in awide range of disciplines. Provides the first comprehensive reference manual for the Rlanguage, including practical guidance and full coverage of thegraphics facilities. Introduces all the statistical models covered by R, beginningwith simple classical tests such as chi-square and t-test. Proceeds to examine more advance methods, from regression andanalysis of variance, through to generalized linear models,generalized mixed models, time series, spatial statistics,multivariate statistics and much more. The R Book is aimed at undergraduates, postgraduates andprofessionals in science, engineering and medicine. It is alsoideal for students and professionals in statistics, economics,geography and the social sciences.