Permutation Methods (Hardcover)

Name: Permutation Methods
Author: Kenneth J. Berry, Paul W. Mielke

A Distance Function Approach

von Kenneth J. Berry, Paul W. Mielke Verlag: Springer US

2nd ed. 2007 - Erschienen 10.08.2007 - Gebunden, 460 Seiten, 241mm x 160mm x 30mm, Sprache(n): eng

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Classical statistical analysis depends on many assumptions about the nature of data and the population from which the samples were drawn. Permutation tests depend only on the data gathered and, although computationally intensive, provide powerful alternatives to conventional analysis techniques. Most commonly-used parametric and permutation statistical tests, such as the matched-pairs t test and analysis of variance, are based on non-metric squared distance functions that have very poor robustness characteristics. This Second Edition places increased emphasis on the use of alternative permutation statistical tests based on metric Euclidean distance functions that have excellent robustness characteristics. These alternative permutation techniques provide many powerful multivariate tests including multivariate multiple regression analyses.

Most commonly-used parametric and permutation statistical tests, such as the matched-pairs t test and analysis of variance, are based on non-metric squared distance functions that have very poor robustness characteristics. This second edition places increased emphasis on the use of alternative permutation statistical tests based on metric Euclidean distance functions that have excellent robustness characteristics. These alternative permutation techniques provide many powerful multivariate tests including multivariate multiple regression analyses. In addition to permutation techniques described in the first edition, this second edition also contains various new permutation statistical methods and studies that include resampling multiple contingency table analyses, analysis concerns involving log-linear models with small samples, an exact discrete analog of Fisher¿s continuous method for combining P-values that arise from small data sets, multiple dichotomous response analyses, problems regarding Fisher¿s Z transformation for correlation analyses, and multivariate similarity comparisons between corresponding multiple categories of two samples.

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