Mathematical Foundations of Infinite-Dimensional Statistical Models - Couverture rigide

Livre 29 sur 46: Cambridge Series in Statistical and Probabilistic Mathematics

Gin�, Evarist ; Nickl, Richard

9781107043169: Mathematical Foundations of Infinite-Dimensional Statistical Models

Couverture rigide

ISBN 10 : 1107043166 ISBN 13 : 9781107043169

Editeur : Cambridge University Press, 2015

Afficher les exemplaires de cette �dition comportant l?ISBN

0 D'occasion

16 Neuf

De EUR 131,16

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, on approximation and wavelet theory, and on the basic theory of function spaces. The theory of statistical inference in such models - hypothesis testing, estimation and confidence sets - is then 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 the 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.

Les informations fournies dans la section � Synopsis � peuvent faire r�f�rence � une autre �dition de ce titre.

� propos des auteurs

Evarist Gin� (1944–2015) was Head of the Department of Mathematics at the University of Connecticut. Gin� was a distinguished mathematician who worked on mathematical statistics and probability in infinite dimensions. He was the author of two books and more than 100 articles.

Richard Nickl is a Reader in Mathematical Statistics in the Statistical Laboratory within the Department of Pure Mathematics and Mathematical Statistics at the University of Cambridge.

Les informations fournies dans la section � A propos du livre � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteur: Cambridge University Press
Date d'�dition: 2015
Langue: anglais
ISBN 10: 1107043166
ISBN 13: 9781107043169
Reliure: Reli�
Nombre de pages: 720
Coordonn�es du fabricant: non disponible
Personne responsable: Avery
https://www.avery.fr/

chemin des peupliers 27
Dardilly
France

Autres �ditions populaires du m�me titre

9781108994132: Mathematical Foundations of Infinite-Dimensional Statistical Models

Edition pr�sent�e

ISBN 10 : 110899413X ISBN 13 : 9781108994132
Editeur : Cambridge University Press, 2021
Couverture souple

R�sultats de recherche pour Mathematical Foundations of Infinite-Dimensional Statistical...

Image d'archives

Mathematical Foundations of Infinite-Dimensional Statistical Models (Hardcover)

Richard Nickl

Edit� par Cambridge University Press, Cambridge, 2015

ISBN 10 : 1107043166 ISBN 13 : 9781107043169

Neuf Couverture rigide

impression � la demande

Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis

�valuation du vendeur 5 sur 5 �toiles

Hardcover. Etat : new. Hardcover. 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, on approximation and wavelet theory, and on the basic theory of function spaces. The theory of statistical inference in such models - hypothesis testing, estimation and confidence sets - is then 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 the 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. High-dimensional and nonparametric statistical models are ubiquitous in modern data science. This book develops a mathematically coherent and objective approach to statistical inference in such models, with a focus on function estimation problems arising from random samples (density estimation) or from Gaussian regression/signal in white noise problems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N� de r�f. du vendeur 9781107043169

Contacter le vendeur

Acheter neuf

EUR 131,16

Livraison gratuite
Exp�dition nationale�: Etats-Unis

Quantit� disponible : 1 disponible(s)

Ajouter au panier

Image d'archives

Mathematical Foundations of Infinite-Dimensional Statistical Models (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 40)

Gin�, Evarist; Nickl, Richard

Edit� par Cambridge University Press, 2015

ISBN 10 : 1107043166 ISBN 13 : 9781107043169

Neuf Couverture rigide

Vendeur : California Books, Miami, FL, Etats-Unis

�valuation du vendeur 4 sur 5 �toiles

Etat : New. N� de r�f. du vendeur I-9781107043169

Contacter le vendeur

Acheter neuf

EUR 131,17

Livraison gratuite
Exp�dition nationale�: Etats-Unis

Quantit� disponible : Plus de 20 disponibles

Ajouter au panier

Image d'archives

Mathematical Foundations of Infinite-Dimensional Statistical Models (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 40)

Gin�, Evarist; Nickl, Richard

Edit� par Cambridge University Press, 2015

ISBN 10 : 1107043166 ISBN 13 : 9781107043169

Neuf Couverture rigide

Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni

�valuation du vendeur 5 sur 5 �toiles

Etat : New. In. N� de r�f. du vendeur ria9781107043169_new

Contacter le vendeur

Acheter neuf

EUR 120,41

Exp�dition �EUR 13,87
Exp�dition depuis Royaume-Uni vers Etats-Unis

Quantit� disponible : Plus de 20 disponibles

Ajouter au panier

Image d'archives

Mathematical Foundations of Infinite-dimensional Statistical Models

Gin�, Evarist/ Nickl, Richard

Edit� par Cambridge Univ Pr, 2016

ISBN 10 : 1107043166 ISBN 13 : 9781107043169

Neuf Couverture rigide

impression � la demande

Vendeur : Revaluation Books, Exeter, Royaume-Uni

�valuation du vendeur 5 sur 5 �toiles

Hardcover. Etat : Brand New. 1st edition. 720 pages. 10.37x7.04x1.71 inches. In Stock. This item is printed on demand. N� de r�f. du vendeur __1107043166

Contacter le vendeur

Acheter neuf

EUR 132,62

Exp�dition �EUR 17,36
Exp�dition depuis Royaume-Uni vers Etats-Unis

Quantit� disponible : 1 disponible(s)

Ajouter au panier

Image fournie par le vendeur

Mathematical Foundations of Infinite-Dimensional Statistical Models

Evarist Gin�, Richard Nickl

Edit� par Cambridge University Press, GB, 2015

ISBN 10 : 1107043166 ISBN 13 : 9781107043169

Neuf Couverture rigide

Vendeur : Rarewaves.com USA, London, LONDO, Royaume-Uni

�valuation du vendeur 5 sur 5 �toiles

Hardback. Etat : New. 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, on approximation and wavelet theory, and on the basic theory of function spaces. The theory of statistical inference in such models - hypothesis testing, estimation and confidence sets - is then 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 the 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. N� de r�f. du vendeur LU-9781107043169

Contacter le vendeur

Acheter neuf

EUR 172,44

Livraison gratuite
Exp�dition depuis Royaume-Uni vers Etats-Unis

Quantit� disponible : Plus de 20 disponibles

Ajouter au panier

Image fournie par le vendeur

Mathematical Foundations of Infinite-Dimensional Statistical Models

Gin�, Evarist|Nickl, Richard

Edit� par Cambridge University Press, 2017

ISBN 10 : 1107043166 ISBN 13 : 9781107043169

Neuf Couverture rigide

impression � la demande

Vendeur : moluna, Greven, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. High-dimensional and nonparametric statistical models are ubiquitous in modern data science. This book develops a mathematically coherent and objective approach to statistical inference in such models, with a focus on function estimation problems arising fr. N� de r�f. du vendeur 31552177

Contacter le vendeur

Acheter neuf

EUR 127,29

Exp�dition �EUR 48,99
Exp�dition depuis Allemagne vers Etats-Unis

Quantit� disponible : Plus de 20 disponibles

Ajouter au panier

Image d'archives

Mathematical Foundations of Infinite-Dimensional Statistical Models (Hardcover)

Richard Nickl

Edit� par Cambridge University Press, Cambridge, 2015

ISBN 10 : 1107043166 ISBN 13 : 9781107043169

Neuf Couverture rigide

impression � la demande

Vendeur : CitiRetail, Stevenage, Royaume-Uni

�valuation du vendeur 5 sur 5 �toiles

Hardcover. Etat : new. Hardcover. 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, on approximation and wavelet theory, and on the basic theory of function spaces. The theory of statistical inference in such models - hypothesis testing, estimation and confidence sets - is then 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 the 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. High-dimensional and nonparametric statistical models are ubiquitous in modern data science. This book develops a mathematically coherent and objective approach to statistical inference in such models, with a focus on function estimation problems arising from random samples (density estimation) or from Gaussian regression/signal in white noise problems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N� de r�f. du vendeur 9781107043169

Contacter le vendeur