Introduction to nonparametric estimation /

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Bibliographic Details
Author / Creator:	Tsybakov, A. B. (Alexandre B.)
Edition:	English ed.
Imprint:	New York : Springer, c2009.
Description:	1 online resource (xii, 214 p.) : ill.
Language:	English
Series:	Springer series in statistics Springer series in statistics.
Subject:	Nonparametric statistics. Estimation theory. Statistics, Nonparametric. Estimation theory. Nonparametric statistics.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/8888127

Hidden Bibliographic Details
ISBN:	9780387790527 (electronic bk.) 0387790527 9780387790510 0387790519
Notes:	"The French edition of this work that is the basis of this work that is the basis of this expanded edition was translated by Vladimir Zaiats."--P. [i]. Includes bibliographical references (p. [203]-209) and index.
Summary:	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.
Other form:	Print version: Tsybakov, A.B. (Alexandre B.) Introduction to nonparametric estimation. New York : Springer, c2009 9780387790510 0387790519

Table of Contents:

Preface to the English Edition; Preface to the French Edition; Notation; Contents; Nonparametric estimators; Examples of nonparametric models and problems; Kernel density estimators; Fourier analysis of kernel density estimators; Unbiased risk estimation. Cross-validation density estimators; Nonparametric regression. The Nadaraya
Watson estimator; Local polynomial estimators; Projection estimators; Oracles; Unbiased risk estimation for regression; Three Gaussian models; Notes; Exercises; Lower bounds on the minimax risk; Introduction; A general reduction scheme.

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