Introduction to robust estimation and hypothesis testing (Q2825433)

For the first three editions of this book see [Zbl 1270.62051], [Zbl 1113.62036], and [Zbl 0991.62508]. They appeared in 2012, 2005, and 1997 resp. The 12 chapters of this book are headed as follows: 1. Introduction, 2. Foundation for robust methods, 3. Estimating measures of location and scale, 4. Confidence intervals in the one-sample case, 5. Comparing two groups, 6. Some multivariate methods, 7. One-way and higher designs for independent groups, 8. Comparing multiple dependent groups, 9. Correlation and tests of independence, 10. Robust regression, 11. More regression methods, 12. ANCOVA, abbreviating for: analysis of covariance. References and Index close this valuable monograph.NEWLINENEWLINEMore details to the introduction: It starts discussing the problems with assuming normality. It is shown that the contamination of one normal distribution by another normal distribution leads to surprisingly large deviations from one pure normal distribution. Then the importance of the central limit theorem is outlined, and the robustness of ANOVA (analysis of variance) is discussed. Next, regression, software R, and data sets are mentioned.NEWLINENEWLINEThere are more than 900 references, including also most recent research results, and more than 100 of them being written by Rand Wilcox, the author of this book.NEWLINENEWLINEPublisher's description: ``The book is a `how-to' on the application of robust methods using available software. Modern robust methods provide improved techniques for dealing with outliers, skewed distribution curvature and heteroscedasticity that can provide substantial gains in power as well as a deeper, more accurate and more nuanced understanding of data. Since the last edition, there have been numerous advances and improvements. They include new techniques for comparing groups and measuring effect size as well as new methods for comparing quantiles. Many new regression methods have been added that include both parametric and nonparametric techniques. The methods related to ANCOVA have been expanded considerably. New perspectives related to discrete distributions with a relatively small sample space are described as well as new results relevant to the shift function. The practical importance of these methods is illustrated using data from real world studies. The R package written for this book now contains over 1200 functions. New to this edition: 35 per cent revised content, covers many new and improved R functions and new techniques that deal with a wide range of situations.''