9781107043169 - mathematical foundations of infinite-dimensional statistical models di giné, evarist; nickl, richard (14 risultati)
Lingua: Inglese
Editore: Cambridge University Press, 2015
Serie: Libro 29 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Da: California Books, Miami, FL, U.S.A.California Books
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Condizione: New.
Lingua: Inglese
Editore: Cambridge University Press, 2015
Serie: Libro 29 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Condizione: New. In.
Lingua: Inglese
Editore: Cambridge University Press, GB, 2015
Serie: Libro 29 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Da: Rarewaves.com USA, London, LONDO, Regno UnitoRarewaves.com USA
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Hardback. Condizione: 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 coheren…t 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.
Lingua: Inglese
Editore: Cambridge University Press CUP, 2015
Serie: Libro 29 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Rilegato
Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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Condizione: New. pp. 720.
Lingua: Inglese
Editore: Cambridge Univ Pr, 2016
Serie: Libro 29 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Rilegato
Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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Hardcover. Condizione: Brand New. 1st edition. 720 pages. 10.37x7.04x1.71 inches. In Stock.
Lingua: Inglese
Editore: Cambridge University Press, 2015
Serie: Libro 29 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Rilegato
Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 th…e 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.
Lingua: Inglese
Editore: Cambridge University Press, GB, 2015
Serie: Libro 29 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Rilegato
Da: Rarewaves.com UK, London, Regno UnitoRarewaves.com UK
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Hardback. Condizione: 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 coheren…t 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.
Lingua: Inglese
Editore: Cambridge University Press, 2015
Serie: Libro 29 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Rilegato
Da: Kennys Bookstore, Olney, MD, U.S.A.Kennys Bookstore
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Condizione: New. This book develops the theory of statistical inference in statistical models with an infinite-dimensional parameter space, including mathematical foundations and key decision-theoretic principles. Series: Cambridge Series in Statistical and Probabilistic Mathematics. Num Pages: 720 pages. BIC Classification: KCH…; PBT; PBWH. Category: (P) Professional & Vocational. Dimension: 253 x 177. . . 2015. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.
Lingua: Inglese
Editore: Cambridge University Press, 2015
Serie: Libro 29 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Rilegato
- Prima edizione
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrlandaKennys Bookshop and Art Galleries Ltd.
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Condizione: New. This book develops the theory of statistical inference in statistical models with an infinite-dimensional parameter space, including mathematical foundations and key decision-theoretic principles. Series: Cambridge Series in Statistical and Probabilistic Mathematics. Num Pages: 720 pages. BIC Classification: KCH…; PBT; PBWH. Category: (P) Professional & Vocational. Dimension: 253 x 177. . . 2015. 1st Edition. Hardcover. . . . .
Lingua: Inglese
Editore: Cambridge Univ Pr, 2016
Serie: Libro 29 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Rilegato
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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Hardcover. Condizione: Brand New. 1st edition. 720 pages. 10.37x7.04x1.71 inches. In Stock. This item is printed on demand.
Lingua: Inglese
Editore: Cambridge University Press, Cambridge, 2015
Serie: Libro 29 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Rilegato
- Print on Demand
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Hardcover. Condizione: 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 giv…es 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.
Lingua: Inglese
Editore: Cambridge University Press, 2017
Serie: Libro 29 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Da: moluna, Greven, Germaniamoluna
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Condizione: 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 o…n function estimation problems arising fr.
Lingua: Inglese
Editore: Cambridge University Press, 2015
Serie: Libro 29 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Rilegato
- Print on Demand
Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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Condizione: New. Print on Demand pp. 720.
Lingua: Inglese
Editore: Cambridge University Press, 2015
Serie: Libro 29 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Rilegato
- Print on Demand
Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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Condizione: New. PRINT ON DEMAND pp. 720.
