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Author: Myles Hollander Publisher: John Wiley & Sons ISBN: 1118553292 Category : Mathematics Languages : en Pages : 978
Book Description
Praise for the Second Edition “This book should be an essential part of the personal library of every practicing statistician.”—Technometrics Thoroughly 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 edition New chapters that address density estimation, wavelets, smoothing, ranked set sampling, and Bayesian nonparametrics 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 Nonparametric 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 graduate courses in applied nonparametric statistics.
Author: Myles Hollander Publisher: John Wiley & Sons ISBN: 1118553292 Category : Mathematics Languages : en Pages : 978
Book Description
Praise for the Second Edition “This book should be an essential part of the personal library of every practicing statistician.”—Technometrics Thoroughly 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 edition New chapters that address density estimation, wavelets, smoothing, ranked set sampling, and Bayesian nonparametrics 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 Nonparametric 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 graduate courses in applied nonparametric statistics.
Author: Thomas P. Hettmansperger Publisher: John Wiley & Sons ISBN: Category : Nonparametric statistics Languages : en Pages : 492
Book Description
Offering an alternative to traditional statistical procedures which are based on least squares fitting, the authors cover such topics as one and two sample location models, linear models, and multivariate models. Both theory and applications are examined.
Author: John Kloke Publisher: CRC Press ISBN: 1439873445 Category : Mathematics Languages : en Pages : 283
Book Description
A Practical Guide to Implementing Nonparametric and Rank-Based Procedures Nonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses, including estimation and inference for models ranging from simple location models to general linear and nonlinear models for uncorrelated and correlated responses. The authors emphasize applications and statistical computation. They illustrate the methods with many real and simulated data examples using R, including the packages Rfit and npsm. The book first gives an overview of the R language and basic statistical concepts before discussing nonparametrics. It presents rank-based methods for one- and two-sample problems, procedures for regression models, computation for general fixed-effects ANOVA and ANCOVA models, and time-to-event analyses. The last two chapters cover more advanced material, including high breakdown fits for general regression models and rank-based inference for cluster correlated data. The book can be used as a primary text or supplement in a course on applied nonparametric or robust procedures and as a reference for researchers who need to implement nonparametric and rank-based methods in practice. Through numerous examples, it shows readers how to apply these methods using R.
Author: M.M. Desu Publisher: CRC Press ISBN: 9781584883197 Category : Mathematics Languages : en Pages : 392
Book Description
Balancing the "cookbook" approach of some texts with the more mathematical approach of others, Nonparametric Statistical Methods for Complete and Censored Data introduces commonly used non-parametric methods for complete data and extends those methods to right censored data analysis. Whenever possible, the authors derive their methodology from the general theory of statistical inference and introduce the concepts intuitively for students with minimal backgrounds. Derivations and mathematical details are relegated to appendices at the end of each chapter, which allows students to easily proceed through each chapter without becoming bogged down in a lot of mathematics. In addition to the nonparametric methods for analyzing complete and censored data, the book covers optimal linear rank statistics, clinical equivalence, analysis of block designs, and precedence tests. To make the material more accessible and practical, the authors use SAS programs to illustrate the various methods included. Exercises in each chapter, SAS code, and a clear, accessible presentation make this an outstanding text for a one-semester senior or graduate-level course in nonparametric statistics for students in a variety of disciplines, from statistics and biostatistics to business, psychology, and the social scientists. Prerequisites: Students will need a solid background in calculus and a two-semester course in mathematical statistics.
Author: W. J. Conover Publisher: ISBN: Category : Mathematics Languages : en Pages : 506
Book Description
Probability theory; Statistical inference; Some tests based on the binomial distribution; Contingency tables; Some methods based on ranks; Statistics of the koolmogorov-smirnov type.
Author: Paul H. Kvam Publisher: John Wiley & Sons ISBN: 9780470168691 Category : Mathematics Languages : en Pages : 448
Book Description
A thorough and definitive book that fully addresses traditional and modern-day topics of nonparametric statistics This book presents a practical approach to nonparametric statistical analysis and provides comprehensive coverage of both established and newly developed methods. With the use of MATLAB, the authors present information on theorems and rank tests in an applied fashion, with an emphasis on modern methods in regression and curve fitting, bootstrap confidence intervals, splines, wavelets, empirical likelihood, and goodness-of-fit testing. Nonparametric Statistics with Applications to Science and Engineering begins with succinct coverage of basic results for order statistics, methods of categorical data analysis, nonparametric regression, and curve fitting methods. The authors then focus on nonparametric procedures that are becoming more relevant to engineering researchers and practitioners. The important fundamental materials needed to effectively learn and apply the discussed methods are also provided throughout the book. Complete with exercise sets, chapter reviews, and a related Web site that features downloadable MATLAB applications, this book is an essential textbook for graduate courses in engineering and the physical sciences and also serves as a valuable reference for researchers who seek a more comprehensive understanding of modern nonparametric statistical methods.
Author: Gregory W. Corder Publisher: John Wiley & Sons ISBN: 1118211251 Category : Mathematics Languages : en Pages : 199
Book Description
A practical and understandable approach to nonparametric statistics for researchers across diverse areas of study As the importance of nonparametric methods in modern statistics continues to grow, these techniques are being increasingly applied to experimental designs across various fields of study. However, researchers are not always properly equipped with the knowledge to correctly apply these methods. Nonparametric Statistics for Non-Statisticians: A Step-by-Step Approach fills a void in the current literature by addressing nonparametric statistics in a manner that is easily accessible for readers with a background in the social, behavioral, biological, and physical sciences. Each chapter follows the same comprehensive format, beginning with a general introduction to the particular topic and a list of main learning objectives. A nonparametric procedure is then presented and accompanied by context-based examples that are outlined in a step-by-step fashion. Next, SPSS® screen captures are used to demonstrate how to perform and recognize the steps in the various procedures. Finally, the authors identify and briefly describe actual examples of corresponding nonparametric tests from diverse fields. Using this organized structure, the book outlines essential skills for the application of nonparametric statistical methods, including how to: Test data for normality and randomness Use the Wilcoxon signed rank test to compare two related samples Apply the Mann-Whitney U test to compare two unrelated samples Compare more than two related samples using the Friedman test Employ the Kruskal-Wallis H test to compare more than two unrelated samples Compare variables of ordinal or dichotomous scales Test for nominal scale data A detailed appendix provides guidance on inputting and analyzing the presented data using SPSS®, and supplemental tables of critical values are provided. In addition, the book's FTP site houses supplemental data sets and solutions for further practice. Extensively classroom tested, Nonparametric Statistics for Non-Statisticians is an ideal book for courses on nonparametric statistics at the upper-undergraduate and graduate levels. It is also an excellent reference for professionals and researchers in the social, behavioral, and health sciences who seek a review of nonparametric methods and relevant applications.
Author: Gregory W. Corder Publisher: John Wiley & Sons ISBN: 1118840429 Category : Mathematics Languages : en Pages : 288
Book Description
“…a very useful resource for courses innonparametric statistics in which the emphasis is on applicationsrather than on theory. It also deserves a place in librariesof all institutions where introductory statistics courses aretaught." –CHOICE This Second Edition presents a practical andunderstandable approach that enhances and expands the statisticaltoolset for readers. This book includes: New coverage of the sign test and the Kolmogorov-Smirnovtwo-sample test in an effort to offer a logical and naturalprogression to statistical power SPSS® (Version 21) software and updated screen captures todemonstrate how to perform and recognize the steps in the variousprocedures Data sets and odd-numbered solutions provided in an appendix,and tables of critical values Supplementary material to aid in reader comprehension, whichincludes: narrated videos and screen animations with step-by-stepinstructions on how to follow the tests using SPSS; online decisiontrees to help users determine the needed type of statistical test;and additional solutions not found within the book.
Author: Peter Sprent Publisher: Springer Science & Business Media ISBN: 9400912234 Category : Science Languages : en Pages : 268
Book Description
This book is a practical introduction to statistical techniques called nonpara metric methods. Using examples, we explain assumptions and demonstrate procedures; theory is kept to a minimum. We show how basic problems are tackled and try to clear up common misapprehensions so as to help both students of statistics meeting the methods for the first time and workers in other fields faced with data needing simple but informative analysis. An analogy between experimenters and car drivers describes our aim. Statistical analyses may be done by following a set of rules without understanding their logical basis, but this has dangers. It is like driving a car with no inkling ofhow the internal combustion engine, the gears, the ignition system, the brakes actually work. Understanding the rudiments helps one get better performance and makesdrivingsafer;appropriate gearchanges become a way to reduce engine stress, prolong engine life, improve fuel economy, minimize wear on brake linings. Knowing how to change the engine oil or replace worn sparking plugs is notessential for adriver, but it will reduce costs. Learning such basics will not make one a fully fledged mechanic, even less an automotive engineer; but it all contributes to more economical and safer driving, alertingone to the dangers ofbald tyres, aleakingexhaust, worn brake linings.
Author: Larry Wasserman Publisher: Springer Science & Business Media ISBN: 0387306234 Category : Mathematics Languages : en Pages : 272
Book Description
This text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference. The book is aimed at Masters or PhD level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods. It covers a wide range of topics including the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book’s dual approach includes a mixture of methodology and theory.
Author: Myles Hollander
Author: Myles Hollander
Author: Thomas P. Hettmansperger
Author: John Kloke
Author: M.M. Desu
Author: W. J. Conover
Author: Paul H. Kvam
Author: Gregory W. Corder
Author: Gregory W. Corder
Author: Peter Sprent
Author: Larry Wasserman