
Introduction to Robust Estimation and Hypothesis Testing
- 5th Edition - September 18, 2021
- Author: Rand R. Wilcox
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 0 0 9 8 - 8
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 0 0 9 9 - 5
Introduction to Robust Estimating and Hypothesis Testing, Fifth Edition is a useful ‘how-to’ on the application of robust methods utilizing easy-to-use software. This truste… Read more

Purchase options

Institutional subscription on ScienceDirect
Request a sales quoteIntroduction to Robust Estimating and Hypothesis Testing, Fifth Edition is a useful ‘how-to’ on the application of robust methods utilizing easy-to-use software. This trusted resource provides an overview of modern robust methods, including improved techniques for dealing with outliers, skewed distribution curvature, and heteroscedasticity that can provide substantial gains in power. Coverage includes techniques for comparing groups and measuring effect size, current methods for comparing quantiles, and expanded regression methods for both parametric and nonparametric techniques. The practical importance of these varied methods is illustrated using data from real world studies. Over 1700 R functions are included to support comprehension and practice.
- Includes the latest developments in robust regression
- Provides many new, improved and accessible R functions
- Offers comprehensive coverage of ANOVA and ANCOVA methods
Advanced UG / graduate students in mathematics and applied courses
Researchers/Professionals
Robust Statistics, Robust Methods in Statistics
Researchers/Professionals
Robust Statistics, Robust Methods in Statistics
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Preface
- References
- Chapter 1: Introduction
- 1.1. Problems With Assuming Normality
- 1.2. Transformations
- 1.3. The Influence Curve
- 1.4. The Central Limit Theorem
- 1.5. Is the ANOVA F Robust?
- 1.6. Regression
- 1.7. More Remarks
- 1.8. R Software
- 1.9. Some Data Management Issues
- 1.10. Data Sets
- References
- Chapter 2: A Foundation for Robust Methods
- 2.1. Basic Tools for Judging Robustness
- 2.2. Some Measures of Location and Their Influence Function
- 2.3. Measures of Scale
- 2.4. Scale-Equivariant M-Measures of Location
- 2.5. Winsorized Expected Values
- References
- Chapter 3: Estimating Measures of Location and Scale
- 3.1. A Bootstrap Estimate of a Standard Error
- 3.2. Density Estimators
- 3.3. The Sample Median and Trimmed Mean
- 3.4. The Finite Sample Breakdown Point
- 3.5. Estimating Quantiles
- 3.6. An M-Estimator of Location
- 3.7. One-Step M-Estimator
- 3.8. W-Estimators
- 3.9. The Hodges–Lehmann Estimator
- 3.10. Skipped Estimators
- 3.11. Some Comparisons of the Location Estimators
- 3.12. More Measures of Scale
- 3.13. Some Outlier Detection Methods
- 3.14. Exercises
- References
- Chapter 4: Inferences in the One-Sample Case
- 4.1. Problems When Working With Means
- 4.2. The g-and-h Distribution
- 4.3. Inferences About the Trimmed, Winsorized Means
- 4.4. Basic Bootstrap Methods
- 4.5. Inferences About M-Estimators
- 4.6. Confidence Intervals for Quantiles
- 4.7. Empirical Likelihood
- 4.8. Inferences About the Probability of Success
- 4.9. Concluding Remarks
- 4.10. Exercises
- References
- Chapter 5: Comparing Two Groups
- 5.1. The Shift Function
- 5.2. Student's T Test
- 5.3. Comparing Medians and Other Trimmed Means
- 5.4. Inferences Based on a Percentile Bootstrap Method
- 5.5. Comparing Measures of Scale
- 5.6. Permutation Tests
- 5.7. Methods Based on Ranks and the Typical Difference
- 5.8. Comparing Two Independent Binomial and Multinomial Distributions
- 5.9. Comparing Dependent Groups
- 5.10. Exercises
- References
- Chapter 6: Some Multivariate Methods
- 6.1. Generalized Variance
- 6.2. Depth
- 6.3. Some Affine Equivariant Estimators
- 6.4. Multivariate Outlier Detection Methods
- 6.5. A Skipped Estimator of Location and Scatter
- 6.6. Robust Generalized Variance
- 6.7. Multivariate Location: Inference in the One-Sample Case
- 6.8. The Two-Sample Case
- 6.9. Multivariate Density Estimators
- 6.10. A Two-Sample, Projection-Type Extension of the Wilcoxon–Mann–Whitney Test
- 6.11. A Relative Depth Analog of the Wilcoxon–Mann–Whitney Test
- 6.12. Comparisons Based on Depth
- 6.13. Comparing Dependent Groups Based on All Pairwise Differences
- 6.14. Robust Principal Component Analysis
- 6.15. Cluster Analysis
- 6.16. Classification Methods
- 6.17. Exercises
- References
- Chapter 7: One-Way and Higher Designs for Independent Groups
- 7.1. Trimmed Means and a One-Way Design
- 7.2. Two-Way Designs and Trimmed Means
- 7.3. Three-Way Designs, Trimmed Means, and Medians
- 7.4. Multiple Comparisons Based on Medians and Other Trimmed Means
- 7.5. A Random Effects Model for Trimmed Means
- 7.6. Bootstrap Global Tests
- 7.7. M-Measures of Location and a Two-Way Design
- 7.8. Ranked-Based Methods for a One-Way Design
- 7.9. A Rank-Based Method for a Two-Way Design
- 7.10. MANOVA Based on Trimmed Means
- 7.11. Nested Designs
- 7.12. Methods for Binary Data
- 7.13. Exercises
- References
- Chapter 8: Comparing Multiple Dependent Groups
- 8.1. Comparing Trimmed Means
- 8.2. Bootstrap Methods Based on Marginal Distributions
- 8.3. Bootstrap Methods Based on Difference Scores and a Measure of Effect Size
- 8.4. Comments on Which Method to Use
- 8.5. Some Rank-Based Methods
- 8.6. Between-by-Within and Within-by-Within Designs
- 8.7. Three-Way Designs
- 8.8. Exercises
- References
- Chapter 9: Correlation and Tests of Independence
- 9.1. Problems With Pearson's Correlation
- 9.2. Two Types of Robust Correlations
- 9.3. Some Type M Measures of Correlation
- 9.4. Some Type O Correlations
- 9.5. A Test of Independence Sensitive to Curvature
- 9.6. Comparing Correlations: Independent Case
- 9.7. Exercises
- References
- Chapter 10: Robust Regression
- 10.1. Problems With Ordinary Least Squares
- 10.2. The Theil–Sen Estimator
- 10.3. Least Median of Squares
- 10.4. Least Trimmed Squares Estimator
- 10.5. Least Trimmed Absolute Value Estimator
- 10.6. M-Estimators
- 10.7. The Hat Matrix
- 10.8. Generalized M-Estimators
- 10.9. The Coakley–Hettmansperger and Yohai Estimators
- 10.10. Skipped Estimators
- 10.11. Deepest Regression Line
- 10.12. A Criticism of Methods With a High Breakdown Point
- 10.13. Some Additional Estimators
- 10.14. Comments About Various Estimators
- 10.15. Outlier Detection Based on a Robust Fit
- 10.16. Logistic Regression and the General Linear Model
- 10.17. Multivariate Regression
- 10.18. Exercises
- References
- Chapter 11: More Regression Methods
- 11.1. Inferences About Robust Regression Parameters
- 11.2. Comparing the Regression Parameters of J≥2 Groups
- 11.3. Detecting Heteroscedasticity
- 11.4. Curvature and Half-Slope Ratios
- 11.5. Curvature and Non-Parametric Regression
- 11.6. Checking the Specification of a Regression Model
- 11.7. Regression Interactions and Moderator Analysis
- 11.8. Comparing Parametric, Additive, and Non-Parametric Fits
- 11.9. Measuring the Strength of an Association Given a Fit to the Data
- 11.10. Comparing Predictors
- 11.11. Marginal Longitudinal Data Analysis: Comments on Comparing Groups
- 11.12. Exercises
- References
- Chapter 12: ANCOVA
- 12.1. Methods Based on Specific Design Points and a Linear Model
- 12.2. Methods When There Is Curvature and a Single Covariate
- 12.3. Dealing With Two or More Covariates When There Is Curvature
- 12.4. Some Global Tests
- 12.5. Methods for Dependent Groups
- 12.6. Exercises
- References
- References
- References
- Index
- No. of pages: 928
- Language: English
- Edition: 5
- Published: September 18, 2021
- Imprint: Academic Press
- Paperback ISBN: 9780128200988
- eBook ISBN: 9780128200995
RW
Rand R. Wilcox
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.
Affiliations and expertise
University of Southern California, USARead Introduction to Robust Estimation and Hypothesis Testing on ScienceDirect