Thoroughly revised and updated, the new edition of NonparametricStatistical Methods includes additional modern topics andprocedures, more practical data sets, and new problems fromreal-life situations. The book continues to emphasize theimportance of nonparametric methods as a significant branch ofmodern statistics and equips readers with the conceptual andtechnical skills necessary to select and apply the appropriateprocedures for any given situation.
Written by leading statisticians, Nonparametric StatisticalMethods, Third Edition provides readers with crucialnonparametric techniques in a variety of settings, emphasizing theassumptions underlying the methods. The book provides an extensivearray of examples that clearly illustrate how to use nonparametricapproaches for handling one- or two-sample location and dispersionproblems, dichotomous data, and one-way and two-way layoutproblems. In addition, the Third Edition features:
MYLES HOLLANDER is Robert O. Lawton DistinguishedProfessor of Statistics and Professor Emeritus at the Florida StateUniversity in Tallahassee. He served as editor of the Theory andMethods Section of the Journal of the American StatisticalAssociation, 1993–96, and he received the Gottfried E.Noether Senior Scholar Award from the American StatisticalAssociation in 2003.
DOUGLAS A. WOLFE is Professor and Chair Emeritus in theDepartment of Statistics at Ohio State University in Columbus. Heis a two-time recipient of the Ohio State University AlumniDistinguished Teaching Award, in 1973–74 and 1988–89.
ERIC CHICKEN is Associate Professor at the Florida StateUniversity in Tallahassee. He is active in modern nonparametricstatistics research fields, including functional analysis,sequential methods, and complex system applications.
The book focuses on the methods of statistical analysis ofheavy-tailed independent identically distributed random variablesby empirical samples of moderate sizes. It provides a detailedsurvey of classical results and recent developments in the theoryof nonparametric estimation of the probability density function,the tail index, the hazard rate and the renewal function.
Both asymptotical results, for example convergence rates of theestimates, and results for the samples of moderate sizes supportedby Monte-Carlo investigation, are considered. The text isillustrated by the application of the considered methodologies toreal data of web traffic measurements.
This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables.
Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include:A chapter on the analysis of correlated outcome dataA wealth of additional material for topics ranging from Bayesian methods to assessing model fitRich data sets from real-world studies that demonstrate each method under discussionDetailed examples and interpretation of the presented results as well as exercises throughout
Applied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines.
This work is in the Wiley-Dunod Series co-published betweenDunod (www.dunod.com) and JohnWiley and Sons, Ltd.
Currently available in the Series:T. W. Anderson The Statistical Analysis of TimeSeriesT. S. Arthanari & Yadolah Dodge Mathematical Programmingin StatisticsEmil Artin Geometric AlgebraNorman T. J. Bailey The Elements of Stochastic ProcesseswithApplications to the Natural SciencesRobert G. Bartle The Elements of Integration and LebesgueMeasureGeorge E. P. Box & Norman R. Draper EvolutionaryOperation: A Statistical Method for Process ImprovementGeorge E. P. Box & George C. Tiao Bayesian Inference inStatistical AnalysisR. W. Carter Finite Groups of Lie Type: Conjugacy Classesand Complex CharactersR. W. Carter Simple Groups of Lie TypeWilliam G. Cochran & Gertrude M. Cox ExperimentalDesigns, Second EditionRichard Courant Differential and Integral Calculus, VolumeIRichard Courant Differential and Integral Calculus, VolumeIIRichard Courant & D. Hilbert Methods of MathematicalPhysics, Volume IRichard Courant & D. Hilbert Methods of MathematicalPhysics, Volume IID. R. Cox Planning of ExperimentsHarold S. M. Coxeter Introduction to Geometry, SecondEditionCharles W. Curtis & Irving Reiner Representation Theoryof Finite Groups andAssociative AlgebrasCharles W. Curtis & Irving Reiner Methods ofRepresentation Theory with Applications to Finite Groups andOrders, Volume ICharles W. Curtis & Irving Reiner Methods ofRepresentation Theory with Applications to Finite Groups andOrders, Volume IICuthbert Daniel & Fred S. Wood Fitting Equations toData: Computer Analysis of Multifactor Data, SecondEditionBruno de Finetti Theory of Probability, Volume IBruno de Finetti Theory of Probability, Volume IIMorris H. DeGroot Optimal Statistical DecisionsW. Edwards Deming Sample Design in BusinessResearchAmos de Shalit & Herman Feshbach Theoretical NuclearPhysics, Volume 1—Nuclear StructureHarold F. Dodge & Harry G. Romig Sampling InspectionTables: Single and Double SamplingJ. L. Doob Stochastic Processes