Nonparametric Statistical Methods

Myles Hollander 지음

Wiley 2013년 11월 25일 출간

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Praise for the Second Editionldquo;This book should be an essential part of the personal library of every practicing statistician.rdquo;m-TechnometricsThoroughly revised and updated, the new edition of Nonparametric Statistical Methods includes additional modern topics and procedures, more practical data sets, and new problems from real-life situations. The book continues to emphasize the importance of nonparametric methods as a significant branch of modern statistics and equips readers with the conceptual and technical skills necessary to select and apply the appropriate procedures for any given situation.Written by leading statisticians, Nonparametric Statistical Methods, Third Edition provides readers with crucial nonparametric techniques in a variety of settings, emphasizing the assumptions underlying the methods. The book provides an extensive array of examples that clearly illustrate how to use nonparametric approaches for handling one- or two-sample location and dispersion problems, dichotomous data, and one-way and two-way layout problems. In addition, the Third Edition features:The use of the freely available R software to aid in computation and simulation, including many new R programs written explicitly for this new editionNew chapters that address density estimation, wavelets, smoothing, ranked set sampling, and Bayesian nonparametricsProblems that illustrate examples from agricultural science, astronomy, biology, criminology, education, engineering, environmental science, geology, home economics, medicine, oceanography, physics, psychology, sociology, and space scienceNonparametric Statistical Methods, Third Edition is an excellent reference for applied statisticians and practitioners who seek a review of nonparametric methods and their relevant applications. The book is also an ideal textbook for upper-undergraduate and first-year gradua

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Preface xiii1. Introduction 11.1. Advantages of Nonparametric Methods 11.2. The Distribution-Free Property 21.3. Some Real-World Applications 31.4. Format and Organization 61.5. Computing with R 81.6. Historical Background 92. The Dichotomous Data Problem 11Introduction 112.1. A Binomial Test 112.2. An Estimator for the Probability of Success 222.3. A Confidence Interval for the Probability of Success (Wilson) 242.4. Bayes Estimators for the Probability of Success 333. The One-Sample Location Problem 39Introduction 39Paired Replicates Analyses by Way of Signed Ranks 393.1. A Distribution-Free Signed Rank Test (Wilcoxon) 403.2. An Estimator Associated with Wilcoxon&'s Signed Rank Statistic (Hodges&n-Lehmann) 563.3. A Distribution-Free Confidence Interval Based on Wilcoxon&'s Signed Rank Test (Tukey) 59Paired Replicates Analyses by Way of Signs 633.4. A Distribution-Free Sign Test (Fisher) 633.5. An Estimator Associated with the Sign Statistic (Hodges&n-Lehmann) 763.6. A Distribution-Free Confidence Interval Based on the Sign Test (Thompson, Savur) 80One-Sample Data 843.7. Procedures Based on the Signed Rank Statistic 843.8. Procedures Based on the Sign Statistic 903.9. An Asymptotically Distribution-Free Test of Symmetry (Randles&n-Fligner&n-Policello&n-Wolfe, Davis&n-Quade) 94Bivariate Data 1023.10. A Distribution-Free Test for Bivariate Symmetry (Hollander) 1023.11. Efficiencies of Paired Replicates and One-Sample Location Procedures 1124. The Two-Sample Location Problem 115Introduction 1154.1. A Distribution-Free Rank Sum Test (Wilcoxon, Mann and Whitney) 1154.2. An Estimator Associated with Wilcoxon&'s Rank Sum Statistic (Hodges&n-Lehmann) 1364.3. A

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저자(글) Myles Hollander

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MYLES HOLLANDER is Robert O. Lawton Distinguished Professor of Statistics and Professor Emeritus at the Florida State University in Tallahassee. He served as editor of the Theory and Methods Section of the Journal of the American Statistical Association, 1993#150;96, and he received the Gottfried E. Noether Senior Scholar Award from the American Statistical Association in 2003.DOUGLAS A. WOLFE is Professor and Chair Emeritus in the Department of Statistics at Ohio State University in Columbus. He is a two#45;time recipient of the Ohio State University Alumni Distinguished Teaching Award, in 1973#150;74 and 1988#150;89.ERIC CHICKEN is Associate Professor at the Florida State University in Tallahassee. He is active in modern nonparametric statistics research fields, including functional analysis, sequential methods, and complex system applications.