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Nonparametric Monte Carlo Tests and Their Applications, Zhu Lixing



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Автор: Zhu Lixing
Название:  Nonparametric Monte Carlo Tests and Their Applications
Издательство: Springer
Классификация:
ISBN: 0387250387
ISBN-13(EAN): 9780387250380
Обложка/Формат: Paperback
Страницы: 184
Вес: 0.58 кг.
Дата издания: 2005
Серия: Lecture Notes in Statistics
Язык: English
Издание: And ed.
Иллюстрации: 17 illustrations, black and white; xi, 184 p. 17 illus.
Размер: 23.11 x 15.24 x 1.02
Читательская аудитория: Professional & vocational
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: A fundamental issue in statistical analysis is testing the fit of a particular probability model to a set of observed data. Monte Carlo approximation to the null distribution of the test provides a convenient and powerful means of testing model fit. Nonparametric Monte Carlo Tests and Their Applications proposes a new Monte Carlo-based methodology to construct this type of approximation when the model is semistructured. When there are no nuisance parameters to be estimated, the nonparametric Monte Carlo test can exactly maintain the significance level, and when nuisance parameters exist, this method can allow the test to asymptotically maintain the level. The author addresses both applied and theoretical aspects of nonparametric Monte Carlo tests. The new methodology has been used for model checking in many fields of statistics, such as multivariate distribution theory, parametric and semiparametric regression models, multivariate regression models, varying-coefficient models with longitudinal data, heteroscedasticity, and homogeneity of covariance matrices. This book will be of interest to both practitioners and researchers investigating goodness-of-fit tests and resampling approximations.Every chapter of the book includes algorithms, simulations, and theoretical deductions. The prerequisites for a full appreciation of the book are a modest knowledge of mathematical statistics and limit theorems in probability/empirical process theory. The less mathematically sophisticated reader will find Chapters 1, 2 and 6 to be a comprehensible introduction on how and where the new method can apply and the rest of the book to be a valuable reference for Monte Carlo test approximation and goodness-of-fit tests.Lixing Zhu is Associate Professor of Statistics at the University of Hong Kong. He is a winner of the Humboldt Research Award at Alexander-von Humboldt Foundation of Germany and an elected Fellow of the Institute of Mathematical Statistics.From the reviews:These lecture notes discuss several topics in goodness-of-fit testing, a classical area in statistical analysis. … The mathematical part contains detailed proofs of the theoretical results. Simulation studies illustrate the quality of the Monte Carlo approximation. … this book constitutes a recommendable contribution to an active area of current research. Winfried Stute for Mathematical Reviews, Issue 2006...Overall, this is an interesting book, which gives a nice introduction to this new and specific field of resampling methods. Dongsheng Tu for Biometrics, September 2006
Дополнительное описание: Формат: 235x155
Илюстрации: 17
Круг читателей: Researchers and the research students who study statistics and have some knowledge of empirical process theory
Ключевые слова:
Язык: eng
Оглавление: Monte Carlo Tests.- Testing for Multivariate Distributions.- Asymptotics of Goodness-of-fit Tests for Symmetry.- A Test of Dimension-reduction Type for Regressios.- Checking the Adequacy of a Partially Linear Model.- Model Checking for Multivariate Regression Models.- Heteroscedasticity Tests for Regressions.- Checking the Adequacy of a Varying-Coefficients Model in Longitudinal Studies.- On the Mean Residual Life Regression Model.- Homogeneity Testing for Covariance Matrices.





Asymptotic Efficiency of Nonparametric Tests

Автор: Yakov Nikitin
Название: Asymptotic Efficiency of Nonparametric Tests
ISBN: 0521470293 ISBN-13(EAN): 9780521470292
Издательство: Cambridge Academ
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Цена: 10699 р.
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Описание: A unified treatment that presents powerful new methods to evaluate explicitly different kinds of efficiencies.

All of Nonparametric Statistics

Автор: Wasserman
Название: All of Nonparametric Statistics
ISBN: 0387251456 ISBN-13(EAN): 9780387251455
Издательство: Springer
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Цена: 13584 р.
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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 … . the writing is excellent and the author is to be congratulated on the clarity achieved. … 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)

Nonparametric Goodness-of-Fit Testing Under Gaussian Models

Автор: Ingster Yuri, Suslina I.A.
Название: Nonparametric Goodness-of-Fit Testing Under Gaussian Models
ISBN: 0387955313 ISBN-13(EAN): 9780387955315
Издательство: Springer
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Цена: 17241 р.
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Описание: There are two main problems in statistics, estimation theory and hypothesis testing. For the classical finite-parametric case, these problems were studied in parallel. On the other hand, many statistical problems are not parametric in the classical sense; the objects of estimation or testing arefunctions, images, and so on. These can be treated as unknown infinite-dimensional parameters that belongto specific functional sets. This approach to nonparametric estimation under asymptotically minimax setting was started in the 1960s-1970s and was developed very intensively for wide classes of functional sets and loss functions.Nonparametric estimation problems have generated a large literature. On the other hand, nonparametrichypotheses testing problems have not drawn comparable attention in the statistical literature. In this book, the authors develop a modern theory of nonparametric goodness-of-fit testing. The presentation is based on an asymptotic version of the minimax approach. The key element of the theory isthe method of constructing of asymptotically least favorable priors for a wide enough class of nonparametric hypothesis testing problems. These provide methods for the construction of asymptotically optimal, rate optimal, and optimal adaptive test procedures. The book is addressed to mathematical statisticians who are interesting in the theory of nonparametricstatistical inference. It will be of interest to specialists who are dealing with applied nonparametric statistical problems in signal detection and transmission, and technical and mother fields. The material is suitable for graduate courses on mathematical statistics. The book assumes familiarity with probability theory.

Nonparametric Methods in Change Point Problems

Автор: Brodsky, E., Darkhovsky, B.S.
Название: Nonparametric Methods in Change Point Problems
ISBN: 0792321227 ISBN-13(EAN): 9780792321224
Издательство: Springer
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Цена: 9926 р.
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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.

Nonparametric Statistics with Applications to Science and Engineering

Автор: Kvam
Название: Nonparametric Statistics with Applications to Science and Engineering
ISBN: 0470081473 ISBN-13(EAN): 9780470081471
Издательство: Wiley
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Цена: 15015 р.
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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.

Nonparametric Methods in Statistics with SAS Applications

Автор: Korosteleva
Название: Nonparametric Methods in Statistics with SAS Applications
ISBN: 1466580623 ISBN-13(EAN): 9781466580626
Издательство: Taylor&Francis
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Цена: 7044 р.
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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.

Nonparametric Smoothing and Lack-of-Fit Tests

Автор: Hart
Название: Nonparametric Smoothing and Lack-of-Fit Tests
ISBN: 0387949801 ISBN-13(EAN): 9780387949802
Издательство: Springer
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Цена: 17241 р.
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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.

Asymptotic Efficiency of Nonparametric Tests

Автор: Nikitin
Название: Asymptotic Efficiency of Nonparametric Tests
ISBN: 0521115922 ISBN-13(EAN): 9780521115926
Издательство: Cambridge Academ
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Цена: 3220 р.
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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.

Nonparametric Analysis of Longitudinal Data in Factorial Experiments

Автор: Edgar Brunner
Название: Nonparametric Analysis of Longitudinal Data in Factorial Experiments
ISBN: 047144166X ISBN-13(EAN): 9780471441663
Издательство: Wiley
Цена: 11146 р.
Наличие на складе: Поставка под заказ.

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.

Nonparametric Statistics

Автор: Cao
Название: Nonparametric Statistics
ISBN: 3319415816 ISBN-13(EAN): 9783319415819
Издательство: Springer
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Цена: 11494 р.
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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.

Robust Rank-Based and Nonparametric Methods

Автор: Liu
Название: Robust Rank-Based and Nonparametric Methods
ISBN: 3319390635 ISBN-13(EAN): 9783319390635
Издательство: Springer
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Цена: 11494 р.
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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.

Introduction to Nonparametric Statistics for the Biological Sciences Using R

Автор: MacFarland
Название: Introduction to Nonparametric Statistics for the Biological Sciences Using R
ISBN: 3319306332 ISBN-13(EAN): 9783319306339
Издательство: Springer
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Цена: 7000 р.
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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


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