Description
Book SynopsisThe 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 multiva
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
