Description
Robust Statistical Methods with R, Second Edition (2nd Ed.)
Authors: Jurečková Jana, Picek Jan, Schindler Martin
Language: EnglishSubjects for Robust Statistical Methods with R, Second Edition:
Keywords
Minimum Risk Equivariant Estimator; Finite Sample Breakdown Point; R statistical package; Positive Definite Dispersion Matrix; measurement error; Distribution Function; multivariate data; Hellinger Distance; robust statistical methods; Multivariate Parametric Estimation; nonparametric statistical procedures; Middle Order Statistic; Eventual Measurement Errors; Multivariate Quantiles; Linear Rank Statistic; Hadamard Derivative; Asymptotic Relative Efficiency; Finite Fisher Information; Marginal MLE; Qualitatively Robust; Robust Statistical Procedures; Regression Quantile; Minimal Asymptotic Variance; LS Regression; Breakdown Point; Classical Statistical Procedures; Unknown Distribution Function; Asymptotically Normal Distribution; Empirical Probability Distribution; Measurement Error Models
50.12 €
In Print (Delivery period: 14 days).
Add to cart the book of Jurečková Jana, Picek Jan, Schindler MartinPublication date: 06-2021
· 15.6x23.4 cm · Paperback
174.18 €
In Print (Delivery period: 14 days).
Add to cart the book of Jurečková Jana, Picek Jan, Schindler MartinPublication date: 05-2019
· 15.6x23.4 cm · Hardback
Description
/li>Contents
/li>Biography
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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
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
Jana Jurečková is a Professor of Statistics at the Charles University, Prague.