Robust Statistical Methods with R, 2/e (Paperback)

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》第二版系統性地介紹了魯棒統計方法，並強調了新發展和計算方面的重要性。書中包含許多數值示例和關於R環境的註解，並且更新的多變量模型章節中增加了關於在R中可視化多變量數據的內容。新的測量誤差模型中的魯棒程序章節主要集中在排名程序上，這些程序對於誤差不敏感。這本書將成為統計學和數學領域的研究人員和研究生的寶貴資源。

特點：
- 系統性地、實用地介紹了魯棒統計方法
- 嚴謹地介紹了整個魯棒方法的範圍，包括估計器的順序版本、它們的矩收斂性，並比較它們的漸近和有限樣本行為
- 擴展的多變量模型部分包括兩樣本檢驗的可接受性、收縮效應和無偏性
- 示範了在測量誤差模型中排名程序的小敏感性
- 強調計算方面，提供了許多示例和插圖，並在書的網站上提供了作者自己在R軟件中的程序。

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

Jana Jurečková 是布拉格查理大學的統計學教授。