Editore: Cambridge University Press, 2021
ISBN 10: 110899413X ISBN 13: 9781108994132
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Editore: Cambridge University Press, 2021
ISBN 10: 110899413X ISBN 13: 9781108994132
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ISBN 10: 110899413X ISBN 13: 9781108994132
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Editore: Cambridge University Press, 2021
ISBN 10: 110899413X ISBN 13: 9781108994132
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Editore: Cambridge University Press, 2021
ISBN 10: 110899413X ISBN 13: 9781108994132
Lingua: Inglese
Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Editore: Cambridge University Press, 2015
ISBN 10: 1107043166 ISBN 13: 9781107043169
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Da: GreatBookPrices, Columbia, MD, U.S.A.
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Editore: Cambridge University Press, 2021
ISBN 10: 110899413X ISBN 13: 9781108994132
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Editore: Cambridge University Press CUP, 2015
ISBN 10: 1107043166 ISBN 13: 9781107043169
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Da: Books Puddle, New York, NY, U.S.A.
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Da: California Books, Miami, FL, U.S.A.
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Editore: Cambridge University Press, Cambridge, 2015
ISBN 10: 1107043166 ISBN 13: 9781107043169
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Aggiungi al carrelloHardcover. 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 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Editore: Cambridge University Press, 2015
ISBN 10: 1107043166 ISBN 13: 9781107043169
Lingua: Inglese
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 121,24
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ISBN 10: 1107043166 ISBN 13: 9781107043169
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Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 125,31
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ISBN 10: 1107043166 ISBN 13: 9781107043169
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Editore: Cambridge University Press, 2021
ISBN 10: 110899413X ISBN 13: 9781108994132
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 80,76
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Now in paperback: the new classic on the theory of statistical inference in statistical models with an infinite-dimensional parameter space.
Editore: Cambridge University Press, Cambridge, 2015
ISBN 10: 1107043166 ISBN 13: 9781107043169
Lingua: Inglese
Da: CitiRetail, Stevenage, Regno Unito
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Aggiungi al carrelloHardcover. 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 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Editore: Cambridge University Press, Cambridge, 2015
ISBN 10: 1107043166 ISBN 13: 9781107043169
Lingua: Inglese
Da: AussieBookSeller, Truganina, VIC, Australia
EUR 158,45
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Aggiungi al carrelloHardcover. 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 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Da: Revaluation Books, Exeter, Regno Unito
EUR 172,88
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Aggiungi al carrelloHardcover. Condizione: Brand New. 1st edition. 720 pages. 10.37x7.04x1.71 inches. In Stock.
Editore: Cambridge University Press, 2015
ISBN 10: 1107043166 ISBN 13: 9781107043169
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 163,27
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Aggiungi al carrelloBuch. 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 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.
Editore: Cambridge University Press, 2021
ISBN 10: 110899413X ISBN 13: 9781108994132
Lingua: Inglese
Da: Majestic Books, Hounslow, Regno Unito
EUR 75,82
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Editore: Cambridge University Press, 2021
ISBN 10: 110899413X ISBN 13: 9781108994132
Lingua: Inglese
Da: Revaluation Books, Exeter, Regno Unito
EUR 58,37
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Aggiungi al carrelloPaperback. Condizione: Brand New. revised edition. 690 pages. 9.75x6.75x1.35 inches. In Stock. This item is printed on demand.
Editore: Cambridge University Press, 2021
ISBN 10: 110899413X ISBN 13: 9781108994132
Lingua: Inglese
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 80,85
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