Mathematical Foundations of Infinite-Dimensional Statistical Models (Paperback, Revised edition)

In nonparametric and high-dimensional statistical models, the classical Gauss-Fisher-Le Cam theory of the optimality of maximum likelihood estimators and Bayesian posterior inference does not apply, and new foundations and ideas have been developed in the past several decades. This book gives a coherent account of the statistical theory in infinite-dimensional parameter spaces. The mathematical foundations include self-contained 'mini-courses' on the theory of Gaussian and empirical processes, approximation and wavelet theory, and the basic theory of function spaces. The theory of statistical inference in such models - hypothesis testing, estimation and confidence sets - is presented within the minimax paradigm of decision theory. This includes the basic theory of convolution kernel and projection estimation, but also Bayesian nonparametrics and nonparametric maximum likelihood estimation. In a final chapter the theory of adaptive inference in nonparametric models is developed, including Lepski's method, wavelet thresholding, and adaptive inference for self-similar functions. Winner of the 2017 PROSE Award for Mathematics.

General

Imprint:	Cambridge UniversityPress
Country of origin:	United Kingdom
Series:	Cambridge Series in Statistical and Probabilistic Mathematics
Release date:	March 2021
Authors:	Evarist Gin e • Richard Nickl
Dimensions:	251 x 176 x 36mm (L x W x T)
Format:	Paperback - Trade
Pages:	704
Edition:	Revised edition
ISBN-13:	978-1-108-99413-2
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Computing & IT > Applications of computing > Signal processing Books > Business & Economics > Economics > Econometrics > General Books > Science & Mathematics > Mathematics > Calculus & mathematical analysis > Vector & tensor analysis Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	1-108-99413-X
Barcode:	9781108994132

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