A Distribution-Free Theory of Nonparametric Regression, L?szl? Gy?rfi; Michael Kohler; Adam Krzyzak; Harro

Автор: Alexandre B. Tsybakov
Название: Introduction to Nonparametric Estimation
ISBN: 0387790519 ISBN-13(EAN): 9780387790510
Издательство: Springer
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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.

Автор: Klaus Nordhausen; Sara Taskinen
Название: Modern Nonparametric, Robust and Multivariate Methods
ISBN: 3319361295 ISBN-13(EAN): 9783319361291
Издательство: Springer
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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.

Автор: Ghosal, Subhashis.
Название: Fundamentals of Nonparametric Bayesian Inference
ISBN: 0521878268 ISBN-13(EAN): 9780521878265
Издательство: Cambridge Academ
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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.

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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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.

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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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.

Автор: Hans-Georg M?ller
Название: Nonparametric Regression Analysis of Longitudinal Data
ISBN: 038796844X ISBN-13(EAN): 9780387968445
Издательство: Springer
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Описание: This text evolved during a set of lectures given by the author at the Division of Statistics at the University of California, Davis in Fall 1986 and is based on the author`s Habilitationsschrift submitted to the University of Marburg in Spring 1985 as well as on published and unpublished work.

Автор: Nadaraya
Название: Nonparametric Estimation of Probability Densities and Regression Curves
ISBN: 9027727570 ISBN-13(EAN): 9789027727572
Издательство: Springer
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Цена: 13275.00 р.
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Описание: 'Et moi, ..., si. j'avail su comment en revenir. One service mathematics has rendered be human race. It has put common sense back jc n'y scrais point a1U: where it belongs, on the topmost sbelf next Jules Verne to \be dusty canister labelled 'discarded non- TIle series is divergent; therefore we may be sense'. able to do something with it Eric T. Bell O. Heaviside Mathematics is a tool for thought. A highly necessary tool in a world where both feedback and non- linearities abound. Similarly, all kinds of parts of mathematics serve as tools for other parts and for other sciences. Applying a simple rewriting rule to the quote on the right above one finds such statements as: 'One service topology has rendered mathematical physics .. .'; 'One service logic bas rendered com- puter science .. .'; 'One service category theory has rendered mathematics .. .'. All arguably true. And all statements obtainable this way form part of the raison d'etre of this series.

Автор: Groeneboom
Название: Nonparametric Estimation under Shape Constraints
ISBN: 0521864011 ISBN-13(EAN): 9780521864015
Издательство: Cambridge Academ
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Описание: This book treats the latest developments in the theory of order-restricted inference, with special attention to nonparametric methods and algorithmic aspects. Each chapter ends with a set of exercises of varying difficulty. The theory is illustrated with the analysis of real-life data, which are mostly medical in nature.