2nd Edition

Robust Statistical Methods with R, Second Edition

    268 Pages 28 B/W Illustrations
    by Chapman & Hall

    268 Pages 28 B/W Illustrations
    by Chapman & Hall

    268 Pages 28 B/W Illustrations
    by Chapman & Hall

    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.


    • 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


    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


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