Reproducing Kernel Hilbert Spaces in Probability and Statistics

Alain Berlinet, Christine Thomas-Agnan

384 pages, parution le 03/12/2003

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Résumé

The book covers theoretical questions including the latest extension of the formalism (therefore of interest to pure mathematicians), as well as more practical ones such as computational issues. It focuses on some of the more fruitful and promising applications, including statistical signal processing, nonparametric curve estimation, random measures, limit theorems, learning theory and some applications at the fringe between Statistics and Approximation Theory. The intention is to put together topics apparently different but sharing the same background.

The text is geared to graduate students in Statistics, Mathematics or Engineering, or to scientists with an equivalent level. A lot of examples and applications are given and the book contains a broad variety of exercises so that it can be used as a textbook at a postgraduate level.

Written for: Graduate students

Sommaire

Theory
RKHS and Stochastic Process
Nonparametric Curve Estimation
Measures and Random Measures
Miscellaneous Applications
Computational Aspects
A Collection of Examples

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Kluwer Academic Publishers
Auteur(s)	Alain Berlinet, Christine Thomas-Agnan
Parution	03/12/2003
Nb. de pages	384
Format	16 x 24
Couverture	Relié
Poids	715g
Intérieur	Noir et Blanc
EAN13	9781402076794
ISBN13	978-1-4020-7679-4