Książka Mathematical Foundations of Infinite-Dimensional Statistical Models Evarist Giné

Mathematical Foundations of Infinite-Dimensional Statistical Models

Język: Angielski
Oprawa: Twarda
Dostępność: Dostępna u dostawcy
Wysyłamy za 14-21 dni
554.01
In nonparametric and high-dimensional statistical models, the classical Gauss-Fisher-Le Cam theory o...

Informacje o książce

Język
Angielski
Oprawa
Książka - Twarda
Data wydania
2015
strony
720
EAN
9781107043169
ISBN
1107043166
Enbook ID
09300843
Waga
1544
Wymiary
187 x 264 x 45

Pełny opis

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.

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