Nonparametric Statistical Methods

Zusatztext

<p>Praise for the Second Edition<br /> This book should be an essential part of the personal library of every practicing statistician.<i></i>Technometrics</p><p><br /> Thoroughly revised and updated, the new edition of<i>Nonparametric Statistical Methods</i> 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.</p><p>Written by leading statisticians,<i>Nonparametric Statistical Methods, Third Edition</i> 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<i>Third Edition</i> features:</p><ul><li>The use of the freely available R software to aid in computation and simulation, including many new R programs written explicitly for this new edition</li><li>New chapters that address density estimation, wavelets, smoothing, ranked set sampling, and Bayesian nonparametrics</li><li>Problems that illustrate examples from agricultural science, astronomy, biology, criminology, education, engineering, environmental science, geology, home economics, medicine, oceanography, physics, psychology, sociology, and space science</li></ul><i>Nonparametric Statistical Methods, Third Edition</i> 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 graduate courses in applied nonparametric statistics.

Autorenportrait

<p><b>MYLES HOLLANDER</b> 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<i>Journal of the American Statistical Association</i>, 1993?96, and he received the Gottfried E. Noether Senior Scholar Award from the American Statistical Association in 2003.</p><p><b>DOUGLAS A. WOLFE</b> is Professor and Chair Emeritus in the Department of Statistics at Ohio State University in Columbus. He is a two-time recipient of the Ohio State University Alumni Distinguished Teaching Award, in 1973?74 and 1988?89.</p><p><b>ERIC CHICKEN</b> 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.</p>

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