## Introduction to Robust Estimation and Hypothesis TestingThis 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.

### Contents

Chapter 1 Introduction | 1 |

Chapter 2 A Foundation for Robust Methods | 19 |

Chapter 3 Estimating Measures of Location and Scale | 43 |

Chapter 4 Confidence Intervals in the OneSample Case | 105 |

Chapter 5 Comparing Two Groups | 137 |

Chapter 6 Some Multivariate Methods | 203 |

Chapter 7 OneWay and Higher Designs for Independent Groups | 265 |

Chapter 8 Comparing Multiple Dependent Groups | 333 |

Chapter 9 Correlation and Tests of Independence | 383 |

Chapter 10 Robust Regression | 413 |

Chapter 11 More Regression Methods | 467 |

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575 | |

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### Common terms and phrases

95 confidence interval actual probability amount of trimming approximately argument biweight bootstrap samples bootstrap-t boxplot breakdown point Chapter column command comparing correlation corresponding covariance critical value degrees of freedom described in Section difference equal equivariant error term estimate of location example given by Eq goal halfspace depth heavy-tailed distribution heteroscedastic homoscedastic Huber’s independent groups influence function jth group level of factor lognormal distribution M-estimator M-measures matrix measure of location measure of scale median method in Section nearest integer null hypothesis outliers p-value parameters percentage bend percentile bootstrap method performs plot predictor probability coverage probability density function problem quantile random variables regression line relatively robust S-PLUS Function S-PLUS variable sample mean sample sizes simulations skewed slope standard error standard normal distribution test statistic test the hypothesis testing H0 trimmed means type I error variance vector Wilcox Winsorized zero

### Popular passages

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.