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Introduction to Nonparametric Estimation

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ISBN-10: 0387790519

ISBN-13: 9780387790510

Edition: 2009

Authors: Alexandre B. Tsybakov

List price: $169.99
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This book will be useful for researchers and grad students interested in theoretical aspects of smoothing techniques. Many important and useful results on optimal and adaptive estimation are provided. As one of the leading mathematical statisticians working in nonparametrics, the author is an authority on the subject.
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Book details

List price: $169.99
Copyright year: 2009
Publisher: Springer New York
Publication date: 11/26/2008
Binding: Hardcover
Pages: 214
Size: 6.10" wide x 9.25" long x 0.50" tall
Weight: 1.122
Language: English

Nonparametric estimators
Examples of nonparametric models and problems
Kernel density estimators
Mean squared error of kernel estimators
Construction of a kernel of order l
Integrated squared risk of kernel estimators
Lack of asymptotic optimality for fixed density
Fourier analysis of kernel density estimators
Unbiased risk estimation. Cross-validation density estimators
Nonparametric regression. The Nadaraya-Watson estimator
Local polynomial estimators
Pointwise and integrated risk of local polynomial estimators
Convergence in the sup-norm
Projection estimators
Sobolev classes and ellipsoids
Integrated squared risk of projection estimators
Generalizations
Oracles
Unbiased risk estimation for regression
Three Gaussian models
Notes
Exercises
Lower bounds on the minimax risk
Introduction
A general reduction scheme
Lower bounds based on two hypotheses
Distances between probability measures
Inequalities for distances
Bounds based on distances
Lower bounds on the risk of regression estimators at a point
Lower bounds based on many hypotheses
Lower bounds in L[subscript 2]
Lower bounds in the sup-norm
Other tools for minimax lower bounds
Fano's lemma
Assouad's lemma
The van Trees inequality
The method of two fuzzy hypotheses
Lower bounds for estimators of a quadratic functional
Notes
Exercises
Asymptotic efficiency and adaptation
Pinsker's theorem
Linear minimax lemma
Proof of Pinsker's theorem
Upper bound on the risk
Lower bound on the minimax risk
Stein's phenomenon
Stein's shrinkage and the James-Stein estimator
Other shrinkage estimators
Superefficiency
Unbiased estimation of the risk
Oracle inequalities
Minimax adaptivity
Inadmissibility of the Pinsker estimator
Notes
Exercises
Appendix
Bibliography
Index