Robust Statistical Methods with R, Second Edition
- 254 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Robust Statistical Methods with R, Second Edition
About this book
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
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Information
Table of contents
- Cover
- Half Title
- Title Page
- Copyright Page
- Contents
- Preface
- Preface to the 1st edition
- Acknowledgments
- Introduction
- 1. Mathematical tools of robustness
- 2. Characteristics of robustness
- 3. Estimation of real parameter
- 4. Linear model
- 5. Multivariate model
- 6. Large sample and finite sample behavior of robust estimators
- 7. Robust and nonparametric procedures in measurement error models
- Appendix A Authors’ own procedures in R
- Bibliography
- Author index
- Subject index
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