Librería Portfolio

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



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