Introduction to Robust Estimation and Hypothesis Testing

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Academic Press, Jan 5, 2005 - Mathematics - 588 pages
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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

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If your background in mathematics isn't strong, this book may seem daunting. Stick with it. (Perhaps read Wilxocx's FUNDAMENTALS OF MODERN STATISTICAL METHODS first.) The important thing is that the book being reviewed here is one of the greatest bargains a quantitative researcher could possibly get hold of. What you get -- provided that you download the statistical freeware R and read a couple of tutorials about it -- is an astounding treasure trove of keys to modern statistical procedures. 


Chapter 1 Introduction
Chapter 2 A Foundation for Robust Methods
Chapter 3 Estimating Measures of Location and Scale
Chapter 4 Confidence Intervals in the OneSample Case
Chapter 5 Comparing Two Groups
Chapter 6 Some Multivariate Methods
Chapter 7 OneWay and Higher Designs for Independent Groups
Chapter 8 Comparing Multiple Dependent Groups
Chapter 9 Correlation and Tests of Independence
Chapter 10 Robust Regression
Chapter 11 More Regression Methods

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Page 537 - ... see Robustness in Statistics and Nonparametric Statistics: The Field. See also: Linear Hypothesis: Regression (Basics): Linear Hypothesis: Regression (Graphics); Robustness in Statistics; Statistics: The Field; Time Series: ARIMA Methods; Time Series: General Bibliography Benjamini Y 1983 Is the t test really conservative when the parent distribution is long-tailed?
Page 564 - Tyler, DE (1991). Some issues in the robust estimation of multivariate location and scatter. In W. Stahel & S. Weisberg (Eds.), Directions in robust statistics and diagnostics, Part // (pp 327-336).
Page 560 - Ruppert, D. (1992). Computing S-estimators for regression and multivariate location/dispersion. Journal of Computational and Graphical Statistics, 1, 253-270.
Page 538 - Heuristics of instability and stabilization in model selection, Annals of Statistics 24 (6), 2350-2383.
Page 537 - Bjerve, S., and Doksum, K. (1993). Correlation curves: measures of association as functions of covariate values, Annals of Statistics, 21, 890-902.

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About the author (2005)

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.

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