Описание: a very useful resource for courses in nonparametric statistics in which the emphasis is on applications rather than on theory. It also deserves a place in libraries of all institutions where introductory statistics courses are taught.
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
Автор: Wasserman Название: All of Nonparametric Statistics ISBN: 0387251456 ISBN-13(EAN): 9780387251455 Издательство: Springer Рейтинг: Цена: 13584 р. Наличие на складе: Есть у поставщика 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)
Описание: While preserving the clear, accessible style of previous editions, this fourth edition reflects the latest developments in computer-intensive methods that deal with intractable analytical problems and unwieldy data sets. This edition summarizes relevant general statistical concepts and introduces basic ideas of nonparametric or distribution-free methods. Designed experiments, including those with factorial treatment structures, are now the focus of an entire chapter. The book also expands coverage on the analysis of survival data and the bootstrap method. The new final chapter focuses on important modern developments. With numerous exercises, the text offers the student edition of StatXact at a discounted price.
Описание: Gives an introduction to deconvolution problems in nonparametric statistics. This title focuses on methodology (description of the estimation procedures) and theory (minimax convergence rates). It provides an appendix chapter on further results of Fourier analysis.
Автор: Larry Wasserman Название: All of Nonparametric Statistics ISBN: 1441920447 ISBN-13(EAN): 9781441920447 Издательство: Springer Рейтинг: Цена: 13584 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This comprehensive text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference, all set out with exceptional clarity. The book`s dual approach includes a mixture of methodology and theory.
Автор: M. Luz G?miz; K. B. Kulasekera; Nikolaos Limnios; Название: Applied Nonparametric Statistics in Reliability ISBN: 1447126343 ISBN-13(EAN): 9781447126348 Издательство: Springer Рейтинг: Цена: 17764 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume focuses on the latest statistical methods used to estimate the performance measures of reliability systems that operate under different conditions. It includes numerous techniques such as nonparametric estimation and lifetime regression analysis.
Описание: Nonparametric statistics has probably become the leading methodology for researchers performing data analysis. It is nevertheless true that, whereas these methods have already proved highly effective in other applied areas of knowledge such as biostatistics or social sciences, nonparametric analyses in reliability currently form an interesting area of study that has not yet been fully explored. Applied Nonparametric Statistics in Reliability is focused on the use of modern statistical methods for the estimation of dependability measures of reliability systems that operate under different conditions. The scope of the book includes: smooth estimation of the reliability function and hazard rate of non-repairable systems; study of stochastic processes for modelling the time evolution of systems when imperfect repairs are performed; nonparametric analysis of discrete and continuous time semi-Markov processes; isotonic regression analysis of the structure function of a reliability system, and lifetime regression analysis. Besides the explanation of the mathematical background, several numerical computations or simulations are presented as illustrative examples. The corresponding computer-based methods have been implemented using R and MATLAB®. A concrete modelling scheme is chosen for each practical situation and, in consequence, a nonparametric inference procedure is conducted. Applied Nonparametric Statistics in Reliability will serve the practical needs of scientists (statisticians and engineers) working on applied reliability subjects.
Описание: A New Way of Analyzing Object Data from a Nonparametric Viewpoint Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis provides one of the first thorough treatments of the theory and methodology for analyzing data on manifolds. It also presents in-depth applications to practical problems arising in a variety of fields, including statistics, medical imaging, computer vision, pattern recognition, and bioinformatics. The book begins with a survey of illustrative examples of object data before moving to a review of concepts from mathematical statistics, differential geometry, and topology. The authors next describe theory and methods for working on various manifolds, giving a historical perspective of concepts from mathematics and statistics. They then present problems from a wide variety of areas, including diffusion tensor imaging, similarity shape analysis, directional data analysis, and projective shape analysis for machine vision. The book concludes with a discussion of current related research and graduate-level teaching topics as well as considerations related to computational statistics. Researchers in diverse fields must combine statistical methodology with concepts from projective geometry, differential geometry, and topology to analyze data objects arising from non-Euclidean object spaces. An expert-driven guide to this approach, this book covers the general nonparametric theory for analyzing data on manifolds, methods for working with specific spaces, and extensive applications to practical research problems. These problems show how object data analysis opens a formidable door to the realm of big data analysis.
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.
Автор: Cao Название: Nonparametric Statistics ISBN: 3319415816 ISBN-13(EAN): 9783319415819 Издательство: Springer Рейтинг: Цена: 11494 р. Наличие на складе: Есть у поставщика 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.
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