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Nonparametric Comparative Statics and Stability, Hale Douglas, Lady George, Maybee John


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Автор: Hale Douglas, Lady George, Maybee John
Название:  Nonparametric Comparative Statics and Stability
ISBN: 9780691632582
Издательство: Wiley
Классификация:

ISBN-10: 0691632588
Обложка/Формат: Hardcover
Страницы: 254
Вес: 0.53 кг.
Дата издания: 19.04.2016
Серия: Princeton legacy library
Язык: English
Иллюстрации: 6 tables 6 line illus.
Размер: 23.39 x 15.60 x 1.60 cm
Читательская аудитория: Tertiary education (us: college)
Ссылка на Издательство: Link
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Поставляется из: Англии
Описание: The authors, leading researchers in the fields of mathematical economics and methodology, present the first comprehensive synthesis of literature on qualitative and other nonparametric techniques, which are important elements of comparative statics and stability analysis in economic theory. The topics covered show how to assess the comparative stat


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.

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.

Nonparametric Comparative Statics and Stability

Автор: Hale Douglas, Lady George, Maybee John
Название: Nonparametric Comparative Statics and Stability
ISBN: 0691603189 ISBN-13(EAN): 9780691603186
Издательство: Wiley
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Цена: 6336.00 р.
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Описание: The authors, leading researchers in the fields of mathematical economics and methodology, present the first comprehensive synthesis of literature on qualitative and other nonparametric techniques, which are important elements of comparative statics and stability analysis in economic theory. The topics covered show how to assess the comparative stat

An Introduction to nonparametric statistics

Автор: Kolassa, John E.
Название: An Introduction to nonparametric statistics
ISBN: 0367194848 ISBN-13(EAN): 9780367194840
Издательство: Taylor&Francis
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Цена: 14086.00 р.
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Описание: This book presents the theory and practice of non-parametric statistics, with an emphasis on motivating principals. The course is a combination of traditional rank based methods and more computationally-intensive topics like density estimation, kernel smoothers in regression, and robustness. The text is aimed at MS students.

Robust Rank-Based and Nonparametric Methods: Michigan, Usa, April 2015: Selected, Revised, and Extended Contributions

Автор: Liu Regina Y., McKean Joseph W.
Название: Robust Rank-Based and Nonparametric Methods: Michigan, Usa, April 2015: Selected, Revised, and Extended Contributions
ISBN: 3319818090 ISBN-13(EAN): 9783319818092
Издательство: Springer
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Цена: 20962.00 р.
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Описание: 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.

Modern Nonparametric, Robust and Multivariate Methods

Автор: Klaus Nordhausen; Sara Taskinen
Название: Modern Nonparametric, Robust and Multivariate Methods
ISBN: 3319224034 ISBN-13(EAN): 9783319224039
Издательство: Springer
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Цена: 18167.00 р.
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Описание: Written by leading experts in the field, this edited volume brings together the latest findings in the area of nonparametric, robust and multivariate statistical methods. The individual contributions cover a wide variety of topics ranging from univariate nonparametric methods to robust methods for complex data structures.

Asymptotic Nonparametric Statistical Analysis of Stationary Time Series

Автор: Daniil Ryabko
Название: Asymptotic Nonparametric Statistical Analysis of Stationary Time Series
ISBN: 3030125637 ISBN-13(EAN): 9783030125639
Издательство: Springer
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Цена: 6986.00 р.
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Описание: Stationarity is a very general, qualitative assumption, that can be assessed on the basis of application specifics. It is thus a rather attractive assumption to base statistical analysis on, especially for problems for which less general qualitative assumptions, such as independence or finite memory, clearly fail. However, it has long been considered too general to be able to make statistical inference. One of the reasons for this is that rates of convergence, even of frequencies to the mean, are not available under this assumption alone. Recently, it has been shown that, while some natural and simple problems, such as homogeneity, are indeed provably impossible to solve if one only assumes that the data is stationary (or stationary ergodic), many others can be solved with rather simple and intuitive algorithms. The latter include clustering and change point estimation among others. In this volume I summarize these results. The emphasis is on asymptotic consistency, since this the strongest property one can obtain assuming stationarity alone. While for most of the problem for which a solution is found this solution is algorithmically realizable, the main objective in this area of research, the objective which is only partially attained, is to understand what is possible and what is not possible to do for stationary time series. The considered problems include homogeneity testing (the so-called two sample problem), clustering with respect to distribution, clustering with respect to independence, change point estimation, identity testing, and the general problem of composite hypotheses testing. For the latter problem, a topological criterion for the existence of a consistent test is presented. In addition, a number of open problems is presented.

Parametric and Nonparametric Inference for Statistical Dynamic Shape Analysis with Applications

Автор: Chiara Brombin; Luigi Salmaso; Lara Fontanella; Lu
Название: Parametric and Nonparametric Inference for Statistical Dynamic Shape Analysis with Applications
ISBN: 3319263102 ISBN-13(EAN): 9783319263106
Издательство: Springer
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Цена: 6986.00 р.
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Описание: This book considers specific inferential issues arising from the analysis of dynamic shapes with the attempt to solve the problems at hand using probability models and nonparametric tests.

Theory of nonparametric tests

Автор: Dickhaus, Thorsten
Название: Theory of nonparametric tests
ISBN: 3319763148 ISBN-13(EAN): 9783319763149
Издательство: Springer
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Цена: 6986.00 р.
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Описание: This textbook provides a self-contained presentation of the main concepts and methods of nonparametric statistical testing, with a particular focus on the theoretical foundations of goodness-of-fit tests, rank tests, resampling tests, and projection tests.

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.

Prior Processes and Their Applications: Nonparametric Bayesian Estimation

Автор: Phadia Eswar G.
Название: Prior Processes and Their Applications: Nonparametric Bayesian Estimation
ISBN: 3319813706 ISBN-13(EAN): 9783319813707
Издательство: Springer
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Цена: 16769.00 р.
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Описание: After an overview of different prior processes, it examines the now pre-eminent Dirichlet process and its variants including hierarchical processes, then addresses new processes such as dependent Dirichlet, local Dirichlet, time-varying and spatial processes, all of which exploit the countable mixture representation of the Dirichlet process.


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