Описание: Designed for a graduate course in applied statistics, Nonparametric Methods in Statistics with SAS Applications teaches students how to apply nonparametric techniques to statistical data. It starts with the tests of hypotheses and moves on to regression modeling, time-to-event analysis, density estimation, and resampling methods. The text begins with classical nonparametric hypotheses testing, including the sign, Wilcoxon sign-rank and rank-sum, Ansari-Bradley, Kolmogorov-Smirnov, Friedman rank, Kruskal-Wallis H, Spearman rank correlation coefficient, and Fisher exact tests. It then discusses smoothing techniques (loess and thin-plate splines) for classical nonparametric regression as well as binary logistic and Poisson models. The author also describes time-to-event nonparametric estimation methods, such as the Kaplan-Meier survival curve and Cox proportional hazards model, and presents histogram and kernel density estimation methods. The book concludes with the basics of jackknife and bootstrap interval estimation. Drawing on data sets from the author’s many consulting projects, this classroom-tested book includes various examples from psychology, education, clinical trials, and other areas. It also presents a set of exercises at the end of each chapter. All examples and exercises require the use of SAS 9.3 software. Complete SAS codes for all examples are given in the text. Large data sets for the exercises are available on the author’s website.
Описание: This volume deals with non-parametric methods of change point (disorder) detection in random processes and fields. A systematic account is given of up-to-date developments in this rapidly evolving branch of statistics.
Описание: 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.
Описание: A fundamental problem in statistical analysis is checking how well a particular probability model fits a set of observed data. In many settings, nonparametric
smoothing methods provide a convenient and powerful means of testing model fit. Nonparametric Smoothing and Lack-of-Fit Tests explores the use of smoothing methods in testing the
fit of parametric regression models.
The book reviews many of the existing methods for testing lack-of-fit and also proposes a number of new methods. Both applied and
theoretical aspects of the model checking problems are addressed. As such, the book should be of interest to practitioners of statistics and researchers investigating either lack-of-fit
tests or nonparametric smoothing ideas.
The first four chapters of the book are an introduction to the problem of estimating regression functions by nonparametric smoothers,
primarily those of kernel and Fourier series type. This part of the book could be used as the foundation for a graduate level course on nonparametric function estimation. The
prerequisites for a full appreciation of the book are a modest knowledge of calculus and some familiarity with the basics of mathematical statistics.
The less mathematically
sophisticated reader will find Chapter 2 to be a comprehensible introduction to smoothing ideas and the rest of the book to be a valuable reference for both nonparametric function
estimation and lack-of-fit tests. Jeffrey D. Hart is Pr
fessor of Statistics at Texas A&M University.
He is an associate editor of the Journal of the American Statistical Association, an elected Fellow of the Institute of Mathematical
Statistics, and winner of a distinguished teaching award at Texas A&M University.
Описание: Making a substantiated choice of the most efficient statistical test is one of the basic problems of statistics. Asymptotic efficiency is an indispensable technique for comparing and ordering statistical tests in large samples. It is especially useful in nonparametric statistics where it is usually necessary to rely on heuristic tests. This monograph presents a unified treatment of the analysis and calculation of the asymptotic efficiencies of nonparametric tests. Powerful new methods are developed to evaluate explicitly different kinds of efficiencies. Of particular interest is the description of domains of the Bahadur local optimality and related characterisation problems based on recent research by the author. Other Russian results are also published here for the first time in English. Researchers, professionals and students in statistics will find this book invaluable.
Автор: Yakov Nikitin Название: Asymptotic Efficiency of Nonparametric Tests ISBN: 0521470293 ISBN-13(EAN): 9780521470292 Издательство: Cambridge Academ Рейтинг: Цена: 13420 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A unified treatment that presents powerful new methods to evaluate explicitly different kinds of efficiencies.
Описание: This title provides a unified approach for the analysis of factorial designs involving longitudinal data that is appropriate for metric data, count data, ordered categorical data, and dichotomous data.
Автор: Wasserman Название: All of Nonparametric Statistics ISBN: 0387251456 ISBN-13(EAN): 9780387251455 Издательство: Springer Рейтинг: Цена: 15014 р. Наличие на складе: Поставка под заказ.
Описание: The goal of this text is to provide the reader with a single book where they can find a brief account of many, modern topics in nonparametric inference. The book is aimed at Master's level or Ph.D. 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.This text 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 has a mixture of methods and theory.From the reviews:"...The book is excellent." (Short Book Reviews of the ISI, June 2006)"Now we have All of Nonparametric Statistics вЂ¦ . the writing is excellent and the author is to be congratulated on the clarity achieved. вЂ¦ the book is excellent." (N.R. Draper, Short Book Reviews, Vol. 26 (1), 2006)"Overall, I enjoyed reading this book very much. I like Wasserman's intuitive explanations and careful insights into why one path or approach is taken over another. Most of all, I am impressed with the wealth of information on the subject of asymptotic nonparametric inferences." (Stergios B. Fotopoulos for Technometrics, Vol. 49, No. 1., February 2007)
Автор: Cao Название: Nonparametric Statistics ISBN: 3319415816 ISBN-13(EAN): 9783319415819 Издательство: Springer Рейтинг: Цена: 12704 р. Наличие на складе: Поставка под заказ.
Описание: This volume collects selected, peer-reviewed contributions from the 2nd Conference of the International Society for Nonparametric Statistics (ISNPS), held in C?diz (Spain) between June 11–16 2014, and sponsored by the American Statistical Association, the Institute of Mathematical Statistics, the Bernoulli Society for Mathematical Statistics and Probability, the Journal of Nonparametric Statistics and Universidad Carlos III de Madrid.The 15 articles are a representative sample of the 336 contributed papers presented at the conference. They cover topics such as high-dimensional data modelling, inference for stochastic processes and for dependent data, nonparametric and goodness-of-fit testing, nonparametric curve estimation, object-oriented data analysis, and semiparametric inference.The aim of the ISNPS 2014 conference was to bring together recent advances and trends in several areas of nonparametric statistics in order to facilitate the exchange of research ideas, promote collaboration among researchers from around the globe, and contribute to the further development of the field.
Описание: The contributors to this volume include many of the distinguished researchers in this area. Many of these scholars have collaborated with Joseph McKean to develop underlying theory for these methods, obtain small sample corrections, and develop efficient algorithms for their computation. The papers cover the scope of the area, including robust nonparametric rank-based procedures through Bayesian and big data rank-based analyses. Areas of application include biostatistics and spatial areas. Over the last 30 years, robust rank-based and nonparametric methods have developed considerably. These procedures generalize traditional Wilcoxon-type methods for one- and two-sample location problems. Research into these procedures has culminated in complete analyses for many of the models used in practice including linear, generalized linear, mixed, and nonlinear models. Settings are both multivariate and univariate. With the development of R packages in these areas, computation of these procedures is easily shared with readers and implemented. This book is developed from the International Conference on Robust Rank-Based and Nonparametric Methods, held at Western Michigan University in April 2015.
Описание: This book contains a rich set of tools for nonparametric analyses, and the purpose of this supplemental text is to provide guidance to students and professional researchers onhow R is used for nonparametric data analysis in the biological sciences:To introduce when nonparametricapproaches to data analysis are appropriateTo introduce the leadingnonparametric tests commonly used in biostatistics and how R is used togenerate appropriate statistics for each testTo introduce common figurestypically associated with nonparametric data analysis and how R is used togenerate appropriate figures in support of each data setThe book focuses on how R is used todistinguish between data that could be classified as nonparametric as opposedto data that could be classified as parametric, with both approaches to data classification covered extensively.Following an introductory lesson on nonparametric statistics for the biological sciences, the book is organized into eight self-contained lessons on various analyses and tests using R to broadly compare differences between data sets and statistical approach.This supplemental text is intended for:Upper-level undergraduate and graduate students majoring in the biological sciences, specifically those in agriculture, biology, and health science - both students in lecture-type courses and also those engaged in research projects, such as a master's thesis or a doctoral dissertationAnd biological researchers at the professional level without a nonparametric statistics background but who regularly work with data more suitable to a nonparametric approach to data analysis
Описание: This book introduces several topics related to linear model theory: multivariate linear models, discriminant analysis, principal components, factor analysis, time series in both the frequency and time domains, and spatial data analysis. The second edition adds new material on nonparametric regression, response surface maximization, and longitudinal models. The book provides a unified approach to these disparate subject and serves as a self-contained companion volume to the author's Plane Answers to Complex Questions: The Theory of Linear Models. Ronald Christensen is Professor of Statistics at the University of New Mexico. He is well known for his work on the theory and application of linear models having linear structure. He is the author of numerous technical articles and several books and he is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics. Also Available: Christensen, Ronald. Plane Answers to Complex Questions: The Theory of Linear Models, Second Edition (1996). New York: Springer-Verlag New York, Inc. Christensen, Ronald. Log-Linear Models and Logistic Regression, Second Edition (1997). New York: Springer-Verlag New York, Inc.
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