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 ...
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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.
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