Adaptive Tests of Significance Using Permutations of Residuals with R and SAS

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John Wiley & Sons, Mar 13, 2012 - Mathematics - 345 pages
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"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"--

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Smoothing Methods and Normalizing Transformations
A TwoSample Adaptive Test
Permutation Tests with Linear Models
An Adaptive Test for a Subset of Coefficients
More Applications of Adaptive Tests
Multicenter and CrossOver Trials
Adaptive Multivariate Tests
Analysis of Repeated Measures Data
Adaptive Confidence Intervals and Estimates
R Code for Univariate Adaptive Tests
SAS Macro for Multiple Comparisons Procedures
R Code for Adaptive Test with Paired Data
R Code for Multivariate Adaptive Tests
SAS Macro for Confidence Intervals
SAS Macro for Estimates

RankBased Tests of Significance

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About the author (2012)

Thomas W. O'gorman, PhD, is Associate Professor in the Department of Mathematical Sciences at Northern Illinois University. Dr. O'Gorman's current research focuses on the analysis of adaptive methods for performing statistical tests and confidence intervals.

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