Introduction to Robust Estimation and Hypothesis Testing
- 3rd Edition - October 30, 2018
- Author: Rand R. Wilcox
- Language: English
- Hardback ISBN:9 7 8 - 0 - 1 2 - 3 8 6 9 8 3 - 8
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 1 0 2 5 6 - 5
- eBook ISBN:9 7 8 - 0 - 1 2 - 3 8 7 0 1 5 - 5
This revised book provides a thorough explanation of the foundation of robust methods, incorporating the latest updates on R and S-Plus, robust ANOVA (Analysis of Variance) and re… Read more
This revised book provides a thorough explanation of the foundation of robust methods, incorporating the latest updates on R and S-Plus, robust ANOVA (Analysis of Variance) and regression. It guides advanced students and other professionals through the basic strategies used for developing practical solutions to problems, and provides a brief background on the foundations of modern methods, placing the new methods in historical context. Author Rand Wilcox includes chapter exercises and many real-world examples that illustrate how various methods perform in different situations.
Introduction to Robust Estimation and Hypothesis Testing, Second Edition, focuses on the practical applications of modern, robust methods which can greatly enhance our chances of detecting true differences among groups and true associations among variables.
- Covers latest developments in robust regression
- Covers latest improvements in ANOVA
- Includes newest rank-based methods
- Describes and illustrated easy to use software
Advanced graduate students interested in applying cutting-edge methods for analyzing data. Also, any applied researcher who uses ANOVA or regression will benefit. A typical course would be Quantitative Methods found in Mathematics, Economics, Health and Biological Sciences and Psychology departments
Preface
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 Using the Computer: R
1.9 Some Data Management Issues
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
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 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
Chapter 4. Confidence Intervals 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 and 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 Concluding Remarks
4.9 Exercises
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 Inferences About a Probabilistic Measure of Effect Size
5.8 Comparing Two Independent Binomials
5.9 Comparing Dependent Groups
5.10 Exercises
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 Inference in the One-Sample Case
6.8 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 Components Analysis
6.15 Cluster Analysis
6.16 Exercises
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 and Trimmed Means
7.4 Multiple Comparisons Based on Medians and Other Trimmed Means
7.5 A Random Effects Model for Trimmed Means
7.6 Global Tests Based on M-Measures of Location
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 Exercises
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
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 Some Rank-Based Multivariate Methods
8.8 Three-Way Designs
8.9 Exercises
Chapter 9. Correlation and Tests of Independence
9.1 Problems with the Product Moment 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
Chapter 10. Robust Regression
10.1 Problems with Ordinary Least Squares
10.2 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
Chapter 11. More Regression Methods
11.1 Inferences About Robust Regression Parameters
11.2 Comparing the Parameters of Two Independent Groups
11.3 Detecting Heteroscedasticity
11.4 Curvature and Half-Slope Ratios
11.5 Curvature and Nonparametric Regression
11.6 Checking the Specification of a Regression Model
11.7 Regression Interactions and Moderator Analysis
11.8 Comparing Parametric, Additive, and Nonparametric Fits
11.9 Measuring the Strength of an Association Given a Fit to the Data
11.10 Comparing Predictors
11.11 ANCOVA
11.12 Marginal Longitudinal Data Analysis: Comments on Comparing Groups
11.13 Exercises
Index
- Edition: 3
- Published: October 30, 2018
- Language: English
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