Gine Evarist (87 results)

Condition: good. A copy that has been read, remains in good condition. All pages are intact, and the cover is intact. The spine and cover show signs of wear. Pages can include notes and highlighting and show signs of wear, and the copy can include "From the library of" labels or previous owner inscriptions. 100% GUARANTEE! Shipped with delivery confirmation, if you're not satisfied with purchase please return item! Ships via media mail.…

Broschiert. Condition: Gut. 424 Seiten Der Erhaltungszustand des hier angebotenen Werks ist trotz seiner Bibliotheksnutzung sehr sauber und kann entsprechende Merkmale aufweisen (Rückenschild, Instituts-Stempel.). In ENGLISCHER Sprache. Sprache: Englisch Gewicht in Gramm: 575.

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: New.

Condition: New. In English.

Paperback. Condition: 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, 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.…

Condition: New.

Condition: New. In English.

Condition: As New. Unread book in perfect condition.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Nur Contents aufnehmen.

Paperback. Condition: Brand New. revised edition. 690 pages. 9.75x6.75x1.35 inches. In Stock.

Condition: New. Revised edition NO-PA16APR2015-KAP.

hardcover. Condition: New. In shrink wrap. Looks like an interesting title.

Condition: New. 2021. Revised. Paperback. . . . . . Books ship from the US and Ireland.

Condition: New.

Condition: New.

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: New. In English.

Paperback. Condition: 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, 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.…

Condition: As New. Unread book in perfect condition.

Hardcover. Condition: Brand New. 1st edition. 365 pages. 9.00x6.25x1.00 inches. In Stock.

Hardback. Condition: 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.…

Condition: New.

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: New. pp. 720.

Condition: New.

Gebunden. Condition: New.