An Introduction to Modern Nonparametric Statistics

Author: James J. Higgins
Publisher: Duxbury Press
ISBN: 9780534387754
Format: PDF, ePub
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Guided by problems that frequently arise in actual practice, James Higgins’ book presents a wide array of nonparametric methods of data analysis that researchers will find useful. It discusses a variety of nonparametric methods and, wherever possible, stresses the connection between methods. For instance, rank tests are introduced as special cases of permutation tests applied to ranks. The author provides coverage of topics not often found in nonparametric textbooks, including procedures for multivariate data, multiple regression, multi-factor analysis of variance, survival data, and curve smoothing. This truly modern approach teaches non-majors how to analyze and interpret data with nonparametric procedures using today’s computing technology.

An Introduction to Modern Nonparametric Statistics

Author: James J. Higgins
Publisher:
ISBN: 9787503745508
Format: PDF, Docs
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本书内容包括: 预备知识、单个样本方法、两样本方法、K-样本方法、成对比较及区组设计、趋势及关联的检验、多变量检验、删失数据分析等.

All of Nonparametric Statistics

Author: Larry Wasserman
Publisher: Springer Science & Business Media
ISBN: 9780387306230
Format: PDF, ePub
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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.

A SAS Companion for Nonparametric Statistics

Author: Scott J. Richter
Publisher: Brooks/Cole
ISBN: 9780534422202
Format: PDF, Mobi
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This companion is designed for anyone who desires a guide to using SAS to carry out nonparametric analyses. It can serve as a SAS lab manual for students enrolled in a course covering nonparametric methods where SAS is used for computing. It is also an excellent reference for researchers who currently use SAS and wish to learn about the capabilities of SAS for performing nonparametric analyses and the syntax for implementing the procedures. An important feature of this companion is that all of the SAS examples presented are self-contained and can be entered into SAS, as they appear, and executed. Thus, the user does not have to deal with issues of creating SAS data sets before using the programs. In addition to presenting the SAS code to obtain various nonparametric analyses, brief introductions to the methods themselves are also given. Particular attention is given to how SAS calculates the results it presents, and explanation provided wherever SAS presents results that might be different from what is typically presented in textbooks.

Practical Nonparametric Statistics

Author: W. J. Conover
Publisher:
ISBN: 9780471168515
Format: PDF, ePub, Docs
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Probability theory; Statistical inference; Some tests based on the binomial distribution; Contingency tables; The use of ranks; Statistics of the kolmogorov-smirnov type; Some miscellaneous tests.

Introduction to Nonparametric Regression

Author: K. Takezawa
Publisher: John Wiley & Sons
ISBN: 0471771449
Format: PDF, ePub
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An easy-to-grasp introduction to nonparametric regression This book's straightforward, step-by-step approach provides an excellent introduction to the field for novices of nonparametric regression. Introduction to Nonparametric Regression clearly explains the basic concepts underlying nonparametric regression and features: * Thorough explanations of various techniques, which avoid complex mathematics and excessive abstract theory to help readers intuitively grasp the value of nonparametric regression methods * Statistical techniques accompanied by clear numerical examples that further assist readers in developing and implementing their own solutions * Mathematical equations that are accompanied by a clear explanation of how the equation was derived The first chapter leads with a compelling argument for studying nonparametric regression and sets the stage for more advanced discussions. In addition to covering standard topics, such as kernel and spline methods, the book provides in-depth coverage of the smoothing of histograms, a topic generally not covered in comparable texts. With a learning-by-doing approach, each topical chapter includes thorough S-Plus? examples that allow readers to duplicate the same results described in the chapter. A separate appendix is devoted to the conversion of S-Plus objects to R objects. In addition, each chapter ends with a set of problems that test readers' grasp of key concepts and techniques and also prepares them for more advanced topics. This book is recommended as a textbook for undergraduate and graduate courses in nonparametric regression. Only a basic knowledge of linear algebra and statistics is required. In addition, this is an excellent resource for researchers and engineers in such fields as pattern recognition, speech understanding, and data mining. Practitioners who rely on nonparametric regression for analyzing data in the physical, biological, and social sciences, as well as in finance and economics, will find this an unparalleled resource.

Introduction to Nonparametric Estimation

Author: Alexandre B. Tsybakov
Publisher: Springer Science & Business Media
ISBN: 0387790527
Format: PDF, ePub, Mobi
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Developed from lecture notes and ready to be used for a course on the graduate level, this concise text aims to introduce the fundamental concepts of nonparametric estimation theory while maintaining the exposition suitable for a first approach in the field.

Deconvolution Problems in Nonparametric Statistics

Author: Alexander Meister
Publisher: Springer Science & Business Media
ISBN: 9783540875574
Format: PDF
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Deconvolution problems occur in many ?elds of nonparametric statistics, for example, density estimation based on contaminated data, nonparametric - gression with errors-in-variables, image and signal deblurring. During the last two decades, those topics have received more and more attention. As appli- tions of deconvolution procedures concern many real-life problems in eco- metrics, biometrics, medical statistics, image reconstruction, one can realize an increasing number of applied statisticians who are interested in nonpa- metric deconvolution methods; on the other hand, some deep results from Fourier analysis, functional analysis, and probability theory are required to understand the construction of deconvolution techniques and their properties so that deconvolution is also particularly challenging for mathematicians. Thegeneraldeconvolutionprobleminstatisticscanbedescribedasfollows: Our goal is estimating a function f while any empirical access is restricted to some quantity h = f?G = f(x?y)dG(y), (1. 1) that is, the convolution of f and some probability distribution G. Therefore, f can be estimated from some observations only indirectly. The strategy is ˆ estimating h ?rst; this means producing an empirical version h of h and, then, ˆ applying a deconvolution procedure to h to estimate f. In the mathematical context, we have to invert the convolution operator with G where some reg- ˆ ularization is required to guarantee that h is contained in the invertibility ˆ domain of the convolution operator. The estimator h has to be chosen with respect to the speci?c statistical experiment.

Nonparametric Statistical Methods

Author: Myles Hollander
Publisher: John Wiley & Sons
ISBN: 1118553292
Format: PDF
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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.