## Introduction to Robust Estimation and Hypothesis Testing (Google eBook)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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### Contents

1 | |

23 | |

43 | |

4 Confidence Intervals in the OneSample Case | 103 |

5 Comparing Two Groups | 137 |

6 Some Multivariate Methods | 215 |

7 OneWay and Higher Designs for Independent Groups | 291 |

### Common terms and phrases

0.95 conﬁdence interval amount of trimming ANOVA approximately assumed biweight bootstrap samples bootstrap-t boxplot breakdown point Chapter coefﬁcients column command comparing controlling the probability correlation corresponding covariance matrix critical value default deﬁned degrees of freedom described in Section efﬁciency equal error probability example ﬁt goal halfspace halfspace depth heavy-tailed distribution heteroscedasticity homoscedastic independent groups indicates inﬂuence function kernel levels of Factor linear contrasts list mode M-estimator measure of location measure of scale median method in Section modiﬁcation multiple comparisons multivariate nboot nominal level null hypothesis observations ofthe outliers p-value parameters percentage bend percentile bootstrap method performs plot predictors probability coverage problem quantile random variables randomly sampled regression line reject relatively robust estimators sample mean sample sizes simulations situations skewed slope speciﬁed standard error standard normal distribution test statistic test the hypothesis testing H0 trimmed means type I error variance vector Wilcox Winsorized zero