## Adaptive Tests of Significance Using Permutations of Residuals with R and SAS"This book concerns adaptive tests of significance, which are statistical tests that use the data to modify the test procedures. The modification is used to reduce the influence of outliers. These adaptive tests are attractive because they are often morepowerful than traditional tests, and they are also quite practical since they can be performed quickly on a computer using R code or a SAS macro. This comprehensive book on adaptive tests can be used by students and researchers alike who are not familiarwith adaptive methods. Chapter 1 provides a gentle introduction to the topic, and Chapter 2 presents a description of the basic tools that are used throughout the book. In Chapters 3, 4, and 5, the basic adaptive testing methods are developed, and Chapters 6 and 7 contain many applications of these tests. Chapters 8 and 9 concern adaptive multivariate tests with multivariate regression models, while the rest of the book concerns adaptive rank tests, adaptive confidence intervals, and adaptive correlations. The adaptive tests described in this book have the following properties: the level of significance is maintained at or near [alpha]; they are more powerful than the traditional test, sometimes much more powerful, if the error distribution is long-tailed or skewed; and there is little power loss compared to the traditional tests if the error distribution is normal. Additional topical coverage includes: smoothing and normalizing methods; two-sample adaptive tests; permutation tests with linear models; adaptive tests in linear models; application of adaptive tests; analysis of paired data; adaptive multivariate tests; analysis of repeated measures data; rank-based approaches to testing; adaptive confidence intervals; and adaptive correlation"-- |

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Contents

Smoothing Methods and Normalizing Transformations | 15 |

A TwoSample Adaptive Test | 43 |

Permutation Tests with Linear Models | 75 |

An Adaptive Test for a Subset of Coefficients | 87 |

More Applications of Adaptive Tests | 111 |

Multicenter and CrossOver Trials | 169 |

Adaptive Multivariate Tests | 191 |

Analysis of Repeated Measures Data | 207 |

Adaptive Confidence Intervals and Estimates | 253 |

R Code for Univariate Adaptive Tests | 283 |

SAS Macro for Multiple Comparisons Procedures | 299 |

R Code for Adaptive Test with Paired Data | 305 |

R Code for Multivariate Adaptive Tests | 313 |

SAS Macro for Conﬁdence Intervals | 321 |

SAS Macro for Estimates | 329 |

RankBased Tests of Significance | 235 |

### Other editions - View all

### Common terms and phrases

&nvars adaptive conﬁdence intervals adaptive estimator adaptive test adaptive weighting adaptive WLS test average bandwidth Biining block Chapter complete model compute correlation structure data set deﬁne dependent variables described differences Empirical power empirical signiﬁcance level error distributions F test ﬁnd ﬁrst function group effect HFR test I-igh interaction ith observation kth permutation kurtosis lambda level of signiﬁcance linear model long-tailed long-tailed distributions LR test maintain its level matrix median mixed model test multivariate test normally distributed null hypothesis O’Gorman obtain outliers output p-value pennutation percentiles perform the adaptive permutation matrix permutation methods permutation test Permutations of Residuals population random variables rank scores reduced model regression model researcher sample sizes SAS macro selection statistics shown in Figure simulation study Skew Bi Low smoothed c.d.f. speciﬁed Suppose tdf=4 Sym test of parallelism test statistic tests of signiﬁcance tperm traditional estimator traditional test treatment effect tunperm two-sample test univariate unpermuted vector weighted least squares