Richard nickl (23 resultados)
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Librería: ThriftBooks-Dallas, Dallas, TX, Estados Unidos de AmericaThriftBooks-Dallas
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Paperback. Condición: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.
Idioma: Inglés
Editorial: Cambridge University Press 2021
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 29 de 46. Libro 29 de 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
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Condición: New.
Idioma: Inglés
Editorial: Cambridge University Press 2021
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 29 de 46. Libro 29 de 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
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EUR 65,60
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Condición: As New. Unread book in perfect condition.
Idioma: Inglés
Editorial: Cambridge University Press 2021
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 29 de 46. Libro 29 de 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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EUR 63,93
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Condición: New.
Idioma: Inglés
Editorial: Cambridge University Press 2021
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 29 de 46. Libro 29 de 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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EUR 70,36
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Condición: As New. Unread book in perfect condition.
Idioma: Inglés
Editorial: Cambridge University Press 2015
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 29 de 46. Libro 29 de 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books
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EUR 133,04
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Condición: New.
Idioma: Inglés
Editorial: Cambridge University Press 2015
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 29 de 46. Libro 29 de 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
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EUR 119,85
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Condición: New. In.
Idioma: Inglés
Editorial: Cambridge University Press 2021
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 29 de 46. Libro 29 de 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 84,40
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Taschenbuch. Condición: 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.
Idioma: Inglés
Editorial: Cambridge University Press, GB 2015
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 29 de 46. Libro 29 de 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Librería: Rarewaves.com USA, London, LONDO, Reino UnidoRarewaves.com USA
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EUR 173,29
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Hardback. Condición: 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.
Idioma: Inglés
Editorial: Cambridge University Press CUP 2015
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 29 de 46. Libro 29 de 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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EUR 181,75
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Condición: New. pp. 720.
Idioma: Inglés
Editorial: Cambridge Univ Pr 2016
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 29 de 46. Libro 29 de 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Librería: Revaluation Books, Exeter, , Reino UnidoRevaluation Books
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EUR 183,65
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Hardcover. Condición: Brand New. 1st edition. 720 pages. 10.37x7.04x1.71 inches. In Stock.
Idioma: Inglés
Editorial: Cambridge University Press, GB 2015
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 29 de 46. Libro 29 de 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Librería: Rarewaves.com UK, London, Reino UnidoRarewaves.com UK
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EUR 162,79
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Hardback. Condición: 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.
Idioma: Inglés
Editorial: Cambridge University Press 2015
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 29 de 46. Libro 29 de 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 165,79
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Buch. Condición: 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.
Editorial: Cambridge University Press
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Librería: Academic Book Solutions, Medford, NY, Estados Unidos de AmericaAcademic Book Solutions
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EUR 116,96
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hardcover. Condición: 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.
Idioma: Inglés
Editorial: Cambridge University Press, Cambridge 2021
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 29 de 46. Libro 29 de 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
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EUR 64,67
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Paperback. Condición: 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 multiple locations in the US or from the UK, depending on stock availability.
Idioma: Inglés
Editorial: Cambridge University Press, Cambridge 2021
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 29 de 46. Libro 29 de 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Paperback. Condición: 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.
Idioma: Inglés
Editorial: Cambridge University Press 2021
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 29 de 46. Libro 29 de 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Librería: moluna, Greven, , Alemaniamoluna
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EUR 66,56
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Condición: 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.
Idioma: Inglés
Editorial: Cambridge University Press, Cambridge 2015
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 29 de 46. Libro 29 de 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
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EUR 133,03
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Hardcover. Condición: 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 multiple locations in the US or from the UK, depending on stock availability.
Idioma: Inglés
Editorial: Cambridge Univ Pr 2016
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 29 de 46. Libro 29 de 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Librería: Revaluation Books, Exeter, , Reino UnidoRevaluation Books
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EUR 123,88
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Hardcover. Condición: Brand New. 1st edition. 720 pages. 10.37x7.04x1.71 inches. In Stock. This item is printed on demand.
Idioma: Inglés
Editorial: Cambridge University Press, Cambridge 2015
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 29 de 46. Libro 29 de 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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EUR 135,27
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Hardcover. Condición: 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.
Idioma: Inglés
Editorial: Cambridge University Press 2017
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 29 de 46. Libro 29 de 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Librería: moluna, Greven, , Alemaniamoluna
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EUR 131,84
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Condición: 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.
Idioma: Inglés
Editorial: Cambridge University Press 2015
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 29 de 46. Libro 29 de 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Librería: Majestic Books, Hounslow, , Reino UnidoMajestic Books
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EUR 183,57
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Condición: New. Print on Demand pp. 720.
Idioma: Inglés
Editorial: Cambridge University Press 2015
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 29 de 46. Libro 29 de 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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EUR 184,42
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Condición: New. PRINT ON DEMAND pp. 720.
