Robust Statistical Methods with R, Second Edition
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

1139436311
Robust Statistical Methods with R, Second Edition
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

55.99 In Stock
Robust Statistical Methods with R, Second Edition

Robust Statistical Methods with R, Second Edition

Robust Statistical Methods with R, Second Edition
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Robust Statistical Methods with R, Second Edition

Robust Statistical Methods with R, Second Edition

Overview

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

Product Details

ISBN-13: 9781032092607
Publisher: CRC Press
Publication date: 06/30/2021
Edition description: 2nd ed.
Pages: 268
Product dimensions: 6.12(w) x 9.19(h) x (d)

About the Author

Jana Jurečková is a Professor of Statistics at the Charles University, Prague.

Table of Contents



Introduction

Mathematical tools of robustness

Characteristics of robustness

Estimation of real parameter

Linear model

Multivariate model

Large sample and finite sample behavior of robust estimators

Robust and nonparametric procedures in measurement error models

Appendix A

Bibliography, Subject Index, Author Index

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