Permutation, Parametric and Bootstrap Tests of Hypotheses [electronic resource] / by Phillip Good.
Contributor(s): SpringerLink (Online service)Material type: TextSeries: Springer Series in Statistics: Publisher: New York, NY : Springer New York, 2005Edition: Third EditionDescription: XX, 316 p. 14 illus. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9780387271583Subject(s): Statistics | Statistics | Statistical Theory and Methods | Statistics for Life Sciences, Medicine, Health Sciences | Statistics for Business/Economics/Mathematical Finance/InsuranceAdditional physical formats: Printed edition:: No titleDDC classification: 519.5 LOC classification: QA276-280Online resources: Click here to access online
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A Wide Range of Applications -- Optimal Procedures -- Testing Hypotheses -- Distributions -- Multiple Tests -- Experimental Designs -- Multifactor Designs -- Categorical Data -- Multivariate Analysis -- Clustering in Time and Space -- Coping with Disaster -- Solving the Unsolved and the Insolvable -- Publishing Your Results -- Increasing Computational Efficiency.
This text will equip both practitioners and theorists with the necessary background in testing hypothesis and decision theory to enable innumerable practical applications of statistics. Its intuitive and informal style makes it suitable as a text for both students and researchers. It can serve as the basis a one- or two-semester graduate course as well as a standard handbook of statistical procedures for the practitioners’ desk. Parametric, permutation, and bootstrap procedures for testing hypotheses are developed side by side. The emphasis on distribution-free permutation procedures will enable workers in applied fields to use the most powerful statistic for their applications and satisfy regulatory agency demands for methods that yield exact significance levels, not approximations. Algebra and an understanding of discrete probability will take the reader through all but the appendix, which utilizes probability measures in its proofs. The revised and expanded text of the 3rd edition includes many more real-world illustrations from biology, business, clinical trials, economics, geology, law, medicine, social science and engineering along with twice the number of exercises. Real-world problems of missing and censored data, multiple comparisons, nonresponders, after-the-fact covariates, and outliers are dealt with at length. New sections are added on sequential analysis and multivariate analysis plus a chapter on the exact analysis of multi-factor designs based on the recently developed theory of synchronous permutations. The book's main features include: Detailed consideration of one-, two-, and k-sample tests, contingency tables, clinical trials, cluster analysis, multiple comparisons, multivariate analysis, and repeated measures Numerous practical applications in archeology, biology, business, climatology, clinical trials, economics, education, engineering, geology, law, medicine, and the social sciences Valuable techniques for reducing computation time Practical advice on experimental design Sections on sequential analysis Comparisons among competing bootstrap, parametric, and permutation techniques. From a review of the first edition: "Permutation Tests is a welcome addition to the literature on this subject and will prove a valuable guide for practitioners . . . This book has already become an important addition to my reference library. Those interested in permutation tests and its applications will enjoy reading it." (Journal of the American Statistical Association) From a review of the second edition: "Permutation Tests is superb as a resource for practitioners. The text covers a broad range of topics, and has myriad pointers to topics not directly addressed. . . the book gives guidance and inspiration to encourage developing one’s own perfectly tailored statistics…The writing is fun to read." (John I. Marden).