Permutation tests : a practical guide to resampling methods for testing hypotheses
著者
書誌事項
Permutation tests : a practical guide to resampling methods for testing hypotheses
（Springer series in statistics）
Springer-Verlag, c2000
2nd ed
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注記
Includes bibliographical references (p. 215-256) and indexes
内容説明・目次
内容説明
This book provides a step-by-step manual on the application of permutation tests in biology, business, medicine, science, and engineering. Its intuitive and informal style will ideally suit it as a text for students and researchers whether experienced or coming to these resampling methods for the first time. The real-world problems of missing and censored data, multiple comparisons, nonresponders, after-the-fact covariates, and outliers are dealt with at length. The book's main features include: * detailed consideration of one-, two-, and k-sample tests, contingency tables, experimental design, clinical trials, cluster analysis, multiple comparisons, multivariate data, regression, and sample size reduction; * numerous practical applications in archeology, biology, climatology, economics, education, medicine, and the social sciences; * valuable techniques for reducing computation time; * practical advice on experimental design; * comparisons with bootstrap, parametric, and nonparametric techniques; * an extensive three-part bibliography featuring more than 1,000 articles.
This new edition has more than 100 additional pages, and includes streamlined statistics for the k-sample comparison and analysis of variance plus expanded sections on computational techniques, multiple comparisons, multiple regression, comparing variances, and testing interactions in balanced designs. Comprehensive author and subject indexes, plus an expert-system guide to methods, provide for further ease of use. The invaluable exercises at the end of every chapter have been supplemented with drills and a number of graduate-level thesis problems.
目次
Wide Range of Applications * Optimal Procedures * Testing Hypothesis * Distributions * Multiple Tests * Experimental Designs * Multifactor Designs * Categorical Data * Multivariate Analysis * Clustering in Time and Space * Coping with Disaster * Solving the Insolveable * Publishing Your Results * Increasing Computational Efficiency.
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