Introduction to Robust Estimation and Hypothesis Testing

Informationen zum Autor Rand R. Wilcox has a Ph.D. in psychometrics, and is a professor of psychology at the University of Southern California. Wilcox's main research interests are statistical methods, particularly robust methods for comparing groups and studying associations. He also collaborates with researchers in occupational therapy, gerontology, biology, education and psychology. Wilcox is an internationally recognized expert in the field of Applied Statistics and has concentrated much of his research in the area of ANOVA and Regression. Wilcox is the author of 12 books on statistics and has published many papers on robust methods. He is currently an Associate Editor for four statistics journals and has served on many editorial boards. He has given numerous invited talks and workshops on robust methods. Klappentext Introduction to Robust Estimating and Hypothesis Testing! 4th Editon!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% revised content. Covers many new and improved R functions. New techniques that deal with a wide range of situations Inhaltsverzeichnis 1. Introduction 2. A 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 ...

Klappentext

Introduction to Robust Estimating and Hypothesis Testing, 4th Editon,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% revised content. Covers many new and improved R functions. New techniques that deal with a wide range of situations

Inhalt

1. Introduction 2. A 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