Mathematical foundations of infinite-dimensional statistical models:
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 cohe...
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| Format: | E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2016
|
| Schriftenreihe: | Cambridge series on statistical and probabilistic mathematics
40 |
| Links: | https://doi.org/10.1017/CBO9781107337862 |
| Zusammenfassung: | 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. |
| Umfang: | 1 Online-Ressource (xiv, 690 Seiten) |
| ISBN: | 9781107337862 |
Internformat
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| 520 | |a 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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| spelling | Giné, Evarist 1944- Mathematical foundations of infinite-dimensional statistical models Evarist Giné, Richard Nickl Cambridge Cambridge University Press 2016 1 Online-Ressource (xiv, 690 Seiten) txt c cr Cambridge series on statistical and probabilistic mathematics 40 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. Nickl, Richard 1980- Erscheint auch als Druck-Ausgabe 9781107043169 |
| spellingShingle | Giné, Evarist 1944- Mathematical foundations of infinite-dimensional statistical models |
| title | Mathematical foundations of infinite-dimensional statistical models |
| title_auth | Mathematical foundations of infinite-dimensional statistical models |
| title_exact_search | Mathematical foundations of infinite-dimensional statistical models |
| title_full | Mathematical foundations of infinite-dimensional statistical models Evarist Giné, Richard Nickl |
| title_fullStr | Mathematical foundations of infinite-dimensional statistical models Evarist Giné, Richard Nickl |
| title_full_unstemmed | Mathematical foundations of infinite-dimensional statistical models Evarist Giné, Richard Nickl |
| title_short | Mathematical foundations of infinite-dimensional statistical models |
| title_sort | mathematical foundations of infinite dimensional statistical models |
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