Permutation Methods: A Distance Function Approach
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Bibliographische Detailangaben
1. Verfasser: Mielke, Paul W. (VerfasserIn)
Format: Elektronisch E-Book
Sprache:English
Veröffentlicht: New York, NY Springer New York 2001
Schriftenreihe:Springer Series in Statistics
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Online-Zugang:Volltext
Beschreibung:The introduction of permutation tests by R. A. Fisher relaxed the paramet­ ric structure requirement of a test statistic. For example, the structure of the test statistic is no longer required if the assumption of normality is removed. The between-object distance function of classical test statis­ tics based on the assumption of normality is squared Euclidean distance. Because squared Euclidean distance is not a metric (i. e. , the triangle in­ equality is not satisfied), it is not at all surprising that classical tests are severely affected by an extreme measurement of a single object. A major purpose of this book is to take advantage of the relaxation of the struc­ ture of a statistic allowed by permutation tests. While a variety of distance functions are valid for permutation tests, a natural choice possessing many desirable properties is ordinary (i. e. , non-squared) Euclidean distance. Sim­ ulation studies show that permutation tests based on ordinary Euclidean distance are exceedingly robust in detecting location shifts of heavy-tailed distributions. These tests depend on a metric distance function and are reasonably powerful for a broad spectrum of univariate and multivariate distributions. Least sum of absolute deviations (LAD) regression linked with a per­ mutation test based on ordinary Euclidean distance yields a linear model analysis which controls for type I error
Beschreibung:1 Online-Ressource (XV, 353 p)
ISBN:9781475734492
9781475734515
ISSN:0172-7397
DOI:10.1007/978-1-4757-3449-2

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