Richard Nickl (21 results)
- Softcover
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Paperback. Condition: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.
Language: English
Published by Cambridge University Press, 2021
Series: Book 29 of 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Language: English
Published by Cambridge University Press, 2021
Series: Book 29 of 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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£ 56.02
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Language: English
Published by Cambridge University Press, 2021
Series: Book 29 of 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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£ 53.87
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Language: English
Published by Cambridge University Press, 2021
Series: Book 29 of 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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£ 59.26
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Language: English
Published by Cambridge University Press, 2015
Series: Book 29 of 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Hardcover
Seller: California Books, Miami, FL, U.S.A.California Books
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Language: English
Published by Cambridge University Press, 2015
Series: Book 29 of 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Hardcover
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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Language: English
Published by Cambridge University Press, 2021
Series: Book 29 of 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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£ 74.36
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Taschenbuch. Condition: 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, 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.
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Language: English
Published by Cambridge University Press, GB, 2015
Series: Book 29 of 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Hardcover
Seller: Rarewaves.com USA, London, LONDO, United KingdomRarewaves.com USA
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£ 149.67
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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.
Language: English
Published by Cambridge University Press CUP, 2015
Series: Book 29 of 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Hardcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
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£ 154.45
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Condition: New. pp. 720.
Language: English
Published by Cambridge Univ Pr, 2016
Series: Book 29 of 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Hardcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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£ 152.77
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Hardcover. Condition: Brand New. 1st edition. 720 pages. 10.37x7.04x1.71 inches. In Stock.
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Language: English
Published by Cambridge University Press, GB, 2015
Series: Book 29 of 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Hardcover
Seller: Rarewaves.com UK, London, United KingdomRarewaves.com UK
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£ 137.17
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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.
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Language: English
Published by Cambridge University Press, 2015
Series: Book 29 of 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Hardcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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£ 146.07
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Buch. Condition: 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.
Published by Cambridge University Press
- Hardcover
Seller: Academic Book Solutions, Medford, NY, U.S.A.Academic Book Solutions
Contact seller5-star sellerhardcover. Condition: Acceptable. Damaged Binding, Pages still bound together., A readable copy. All pages are intact, and the cover is intact (the dust cover may be missing). Pages can include notes--in pen or highlighter.
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Language: English
Published by Cambridge University Press, Cambridge, 2021
Series: Book 29 of 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Softcover
- Print on Demand
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
Contact seller5-star sellerCondition: New
£ 58.99
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Paperback. Condition: new. Paperback. In nonparametric and high-dimensional statistical models, the classical GaussFisherLe 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. 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 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.
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Language: English
Published by Cambridge University Press, 2021
Series: Book 29 of 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Softcover
- Print on Demand
Seller: moluna, Greven, Germanymoluna
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£ 58.65
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Condition: 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 on… function estimation problems arising fr.
Language: English
Published by Cambridge Univ Pr, 2016
Series: Book 29 of 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Hardcover
- Print on Demand
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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£ 111.22
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Hardcover. Condition: Brand New. 1st edition. 720 pages. 10.37x7.04x1.71 inches. In Stock. This item is printed on demand.
Language: English
Published by Cambridge University Press, Cambridge, 2015
Series: Book 29 of 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Hardcover
- Print on Demand
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
Contact seller5-star sellerCondition: New
£ 113.99
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Hardcover. Condition: 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 give…s 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.
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Language: English
Published by Cambridge University Press, 2017
Series: Book 29 of 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Hardcover
- Print on Demand
Seller: moluna, Greven, Germanymoluna
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£ 116.17
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Condition: 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 on… function estimation problems arising fr.
Language: English
Published by Cambridge University Press, 2015
Series: Book 29 of 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Hardcover
- Print on Demand
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
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£ 159.63
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Condition: New. Print on Demand pp. 720.
Language: English
Published by Cambridge University Press, 2015
Series: Book 29 of 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Hardcover
- Print on Demand
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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£ 166.95
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Condition: New. PRINT ON DEMAND pp. 720.
