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Nonparametric Statistics, Patrice Bertail; Delphine Blanke; Pierre-Andr? Cor


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Автор: Patrice Bertail; Delphine Blanke; Pierre-Andr? Cor
Название:  Nonparametric Statistics
ISBN: 9783319969404
Издательство: Springer
Классификация:

ISBN-10: 3319969404
Обложка/Формат: Hardcover
Страницы: 390
Вес: 0.76 кг.
Дата издания: 2019
Серия: Springer Proceedings in Mathematics & Statistics
Язык: English
Издание: 1st ed. 2018
Иллюстрации: 23 tables, color; 26 illustrations, color; 27 illustrations, black and white; ix, 390 p. 53 illus., 26 illus. in color.
Размер: 234 x 156 x 22
Читательская аудитория: Professional & vocational
Ключевые слова: Statistical Theory and Methods
Основная тема: Statistics
Подзаголовок: 3rd ISNPS, Avignon, France, June 2016
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: This volume presents the latest advances and trends in nonparametric statistics, and gathers selected and peer-reviewed contributions from the 3rd Conference of the International Society for Nonparametric Statistics (ISNPS), held in Avignon, France on June 11-16, 2016. It covers a broad range of nonparametric statistical methods, from density estimation, survey sampling, resampling methods, kernel methods and extreme values, to statistical learning and classification, both in the standard i.i.d. case and for dependent data, including big data. The International Society for Nonparametric Statistics is uniquely global, and its international conferences are intended to foster the exchange of ideas and the latest advances among researchers from around the world, in cooperation with established statistical societies such as the Institute of Mathematical Statistics, the Bernoulli Society and the International Statistical Institute. The 3rd ISNPS conference in Avignon attracted more than 400 researchers from around the globe, and contributed to the further development and dissemination of nonparametric statistics knowledge.

Дополнительное описание:
Symmetrizing k-nn and Mutual k-nn Smoothers (P. A. Cornillon, A. Gribinski, N. Hengartner, T. Kerdreux and E. Matzner-L?ber).- Multiplicative Bias Corrected Nonparametric Smoothers (N. Hengartner, E. Matzner-L?ber, L. Rouvi?re and T. Burr).- Nonparam



Автор: Larry Wasserman
Название: All of Nonparametric Statistics
ISBN: 1441920447 ISBN-13(EAN): 9781441920447
Издательство: Springer
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Цена: 15372.00 р.
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Описание: 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.

Nonparametric Statistics for Applied Research

Название: Nonparametric Statistics for Applied Research
ISBN: 1461490405 ISBN-13(EAN): 9781461490401
Издательство: Springer
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Цена: 8384.00 р.
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Описание:

​​Non-parametric methods are widely used for studying populations that take on a ranked order (such as movie reviews receiving one to four stars). The use of non-parametric methods may be necessary when data have a ranking but no clear numerical interpretation, such as when assessing preferences. In terms of levels of measurement, non-parametric methods result in ordinal data. As non-parametric methods make fewer assumptions, their applicability is much wider than the corresponding parametric methods. In particular, they may be applied in situations where less is known about the application in question. Also, due to the reliance on fewer assumptions, non-parametric methods are more robust. Non-parametric methods have many popular applications, and are widely used in research in the fields of the behavioral sciences and biomedicine.

This is a textbook on non-parametric statistics for applied research. The authors propose to use a realistic yet mostly fictional situation and series of dialogues to illustrate in detail the statistical processes required to complete data analysis. This book draws on a readers existing elementary knowledge of statistical analyses to broaden his/her research capabilities. The material within the book is covered in such a way that someone with a very limited knowledge of statistics would be able to read and understand the concepts detailed in the text.

The "real world" scenario to be presented involves a multidisciplinary team of behavioral, medical, crime analysis, and policy analysis professionals work together to answer specific empirical questions regarding real-world applied problems. The reader is introduced to the team and the data set, and through the course of the text follows the team as they progress through the decision making process of narrowing the data and the research questions to answer the applied problem. In this way, abstract statistical concepts are translated into concrete and specific language.

This text uses one data set from which all examples are taken. This is radically different from other statistics books which provide a varied array of examples and data sets. Using only one data set facilitates reader-directed teaching and learning by providing multiple research questions which are integrated rather than using disparate examples and completely unrelated research questions and data.

Practical Nonparametric Statistics

Автор: Conover, W.J.
Название: Practical Nonparametric Statistics
ISBN: 0471160687 ISBN-13(EAN): 9780471160687
Издательство: Wiley
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Цена: 36741.00 р.
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Описание: This highly-regarded text serves as a quick reference book which offers clear, concise instructions on how and when to use the most popular nonparametric procedures.

Nonparametric Statistics - A Step-by-Step Approach  2e

Автор: Corder
Название: Nonparametric Statistics - A Step-by-Step Approach 2e
ISBN: 1118840313 ISBN-13(EAN): 9781118840313
Издательство: Wiley
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Цена: 13139.00 р.
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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.

Fundamentals of Nonparametric Bayesian Inference

Автор: Ghosal, Subhashis.
Название: Fundamentals of Nonparametric Bayesian Inference
ISBN: 0521878268 ISBN-13(EAN): 9780521878265
Издательство: Cambridge Academ
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Цена: 12989.00 р.
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Описание: Written by top researchers, this self-contained text is the authoritative account of Bayesian nonparametrics, a nearly universal framework for inference in statistics and machine learning, with practical use in all areas of science, including economics and biostatistics. Appendices with prerequisites and numerous exercises support its use for graduate courses.

Introduction to Nonparametric Estimation

Автор: Alexandre B. Tsybakov
Название: Introduction to Nonparametric Estimation
ISBN: 0387790519 ISBN-13(EAN): 9780387790510
Издательство: Springer
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Цена: 15372.00 р.
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Описание: Presents basic nonparametric regression and density estimators and analyzes their properties. This book covers minimax lower bounds, and develops advanced topics such as: Pinsker`s theorem, oracle inequalities, Stein shrinkage, and sharp minimax adaptivity.

Bayesian Nonparametric Data Analysis

Автор: Muller, P., Quintana, F.A., Jara, A., Hanson, T.
Название: Bayesian Nonparametric Data Analysis
ISBN: 3319189670 ISBN-13(EAN): 9783319189673
Издательство: Springer
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Цена: 11878.00 р.
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Описание: This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones.

All of Nonparametric Statistics

Автор: Wasserman
Название: All of Nonparametric Statistics
ISBN: 0387251456 ISBN-13(EAN): 9780387251455
Издательство: Springer
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Цена: 20962.00 р.
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Описание: 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.

Applied Nonparametric Statistics in Reliability

Автор: G?miz
Название: Applied Nonparametric Statistics in Reliability
ISBN: 0857291173 ISBN-13(EAN): 9780857291172
Издательство: Springer
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Цена: 23757.00 р.
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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.


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