smoothing spline anova models von chong (28 Ergebnisse)

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Taschenbuch. Zustand: Neu. Smoothing Spline ANOVA Models | Chong Gu | Taschenbuch | xviii | Englisch | 2015 | Springer US | EAN 9781489989840 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Buch. Zustand: Neu. Neuware -Nonparametric function estimation with stochastic data, otherwiseknown as smoothing, has been studied by several generations ofstatisticians. Assisted by the ample computing power in today'sservers, desktops, and laptops, smoothing methods have been findingtheir ways into everyday data analysis by practitioners. While scoresof methods have proved successful for univariate smoothing, onespractical in multivariate settings number far less. Smoothing splineANOVA models are a versatile family of smoothing methods derivedthrough roughness penalties, that are suitable for both univariate andmultivariate problems.In this book, the author presents a treatise on penalty smoothingunder a unified framework. Methods are developed for (i) regressionwith Gaussian and non-Gaussian responses as well as with censored lifetime data; (ii) density and conditional density estimation under avariety of sampling schemes; and (iii) hazard rate estimation withcensored life time data and covariates. The unifying themes are thegeneral penalized likelihood method and the construction ofmultivariate models with built-in ANOVA decompositions. Extensivediscussions are devoted to model construction, smoothing parameterselection, computation, and asymptotic convergence.Most of the computational and data analytical tools discussed in thebook are implemented in R, an open-source platform for statisticalcomputing and graphics. Suites of functions are embodied in the Rpackage gss, and are illustrated throughout the book using simulatedand real data examples.This monograph will be useful as a reference work for researchers intheoretical and applied statistics as well as for those in otherrelated disciplines. It can also be used as a text for graduate levelcourses on the subject. Most of the materials are accessibleto asecond year graduate student with a good training in calculus andlinear algebra and working knowledge in basic statistical inferencessuch as linear models and maximum likelihood estimates.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 452 pp. Englisch.

Taschenbuch. Zustand: Neu. Neuware -Nonparametric function estimation with stochastic data, otherwiseknown as smoothing, has been studied by several generations ofstatisticians. Assisted by the ample computing power in today'sservers, desktops, and laptops, smoothing methods have been findingtheir ways into everyday data analysis by practitioners. While scoresof methods have proved successful for univariate smoothing, onespractical in multivariate settings number far less. Smoothing splineANOVA models are a versatile family of smoothing methods derivedthrough roughness penalties, that are suitable for both univariate andmultivariate problems.In this book, the author presents a treatise on penalty smoothingunder a unified framework. Methods are developed for (i) regressionwith Gaussian and non-Gaussian responses as well as with censored lifetime data; (ii) density and conditional density estimation under avariety of sampling schemes; and (iii) hazard rate estimation withcensored life time data and covariates. The unifying themes are thegeneral penalized likelihood method and the construction ofmultivariate models with built-in ANOVA decompositions. Extensivediscussions are devoted to model construction, smoothing parameterselection, computation, and asymptotic convergence.Most of the computational and data analytical tools discussed in thebook are implemented in R, an open-source platform for statisticalcomputing and graphics. Suites of functions are embodied in the Rpackage gss, and are illustrated throughout the book using simulatedand real data examples.This monograph will be useful as a reference work for researchers intheoretical and applied statistics as well as for those in otherrelated disciplines. It can also be used as a text for graduate levelcourses on the subject. Most of the materials are accessibleto asecond year graduate student with a good training in calculus andlinear algebra and working knowledge in basic statistical inferencessuch as linear models and maximum likelihood estimates.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 452 pp. Englisch.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Nonparametric function estimation with stochastic data, otherwiseknown as smoothing, has been studied by several generations ofstatisticians. Assisted by the ample computing power in today'sservers, desktops, and laptops, smoothing methods have been findingtheir ways into everyday data analysis by practitioners. While scoresof methods have proved successful for univariate smoothing, onespractical in multivariate settings number far less. Smoothing splineANOVA models are a versatile family of smoothing methods derivedthrough roughness penalties, that are suitable for both univariate andmultivariate problems.In this book, the author presents a treatise on penalty smoothingunder a unified framework. Methods are developed for (i) regressionwith Gaussian and non-Gaussian responses as well as with censored lifetime data; (ii) density and conditional density estimation under avariety of sampling schemes; and (iii) hazard rate estimation withcensored life time data and covariates. The unifying themes are thegeneral penalized likelihood method and the construction ofmultivariate models with built-in ANOVA decompositions. Extensivediscussions are devoted to model construction, smoothing parameterselection, computation, and asymptotic convergence.Most of the computational and data analytical tools discussed in thebook are implemented in R, an open-source platform for statisticalcomputing and graphics. Suites of functions are embodied in the Rpackage gss, and are illustrated throughout the book using simulatedand real data examples.This monograph will be useful as a reference work for researchers intheoretical and applied statistics as well as for those in otherrelated disciplines. It can also be used as a text for graduate levelcourses on the subject. Most of the materials are accessibleto asecond year graduate student with a good training in calculus andlinear algebra and working knowledge in basic statistical inferencessuch as linear models and maximum likelihood estimates.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Nonparametric function estimation with stochastic data, otherwiseknown as smoothing, has been studied by several generations ofstatisticians. Assisted by the ample computing power in today'sservers, desktops, and laptops, smoothing methods have been findingtheir ways into everyday data analysis by practitioners. While scoresof methods have proved successful for univariate smoothing, onespractical in multivariate settings number far less. Smoothing splineANOVA models are a versatile family of smoothing methods derivedthrough roughness penalties, that are suitable for both univariate andmultivariate problems.In this book, the author presents a treatise on penalty smoothingunder a unified framework. Methods are developed for (i) regressionwith Gaussian and non-Gaussian responses as well as with censored lifetime data; (ii) density and conditional density estimation under avariety of sampling schemes; and (iii) hazard rate estimation withcensored life time data and covariates. The unifying themes are thegeneral penalized likelihood method and the construction ofmultivariate models with built-in ANOVA decompositions. Extensivediscussions are devoted to model construction, smoothing parameterselection, computation, and asymptotic convergence.Most of the computational and data analytical tools discussed in thebook are implemented in R, an open-source platform for statisticalcomputing and graphics. Suites of functions are embodied in the Rpackage gss, and are illustrated throughout the book using simulatedand real data examples.This monograph will be useful as a reference work for researchers intheoretical and applied statistics as well as for those in otherrelated disciplines. It can also be used as a text for graduate levelcourses on the subject. Most of the materials are accessibleto asecond year graduate student with a good training in calculus andlinear algebra and working knowledge in basic statistical inferencessuch as linear models and maximum likelihood estimates.

Hardcover. Zustand: Brand New. 2nd edition. 433 pages. 9.50x6.50x1.00 inches. In Stock.

Buch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Nonparametric function estimation with stochastic data, otherwiseknown as smoothing, has been studied by several generations ofstatisticians. Assisted by the ample computing power in today'sservers, desktops, and laptops, smoothing methods have been findingtheir ways into everyday data analysis by practitioners. While scoresof methods have proved successful for univariate smoothing, onespractical in multivariate settings number far less. Smoothing splineANOVA models are a versatile family of smoothing methods derivedthrough roughness penalties, that are suitable for both univariate andmultivariate problems.In this book, the author presents a treatise on penalty smoothingunder a unified framework. Methods are developed for (i) regressionwith Gaussian and non-Gaussian responses as well as with censored lifetime data; (ii) density and conditional density estimation under avariety of sampling schemes; and (iii) hazard rate estimation withcensored life time data and covariates. The unifying themes are thegeneral penalized likelihood method and the construction ofmultivariate models with built-in ANOVA decompositions. Extensivediscussions are devoted to model construction, smoothing parameterselection, computation, and asymptotic convergence.Most of the computational and data analytical tools discussed in thebook are implemented in R, an open-source platform for statisticalcomputing and graphics. Suites of functions are embodied in the Rpackage gss, and are illustrated throughout the book using simulatedand real data examples.This monograph will be useful as a reference work for researchers intheoretical and applied statistics as well as for those in otherrelated disciplines. It can also be used as a text for graduate levelcourses on the subject. Most of the materials are accessibleto asecond year graduate student with a good training in calculus andlinear algebra and working knowledge in basic statistical inferencessuch as linear models and maximum likelihood estimates. 452 pp. Englisch.

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Nonparametric function estimation with stochastic data, otherwiseknown as smoothing, has been studied by several generations ofstatisticians. Assisted by the ample computing power in today'sservers, desktops, and laptops, smoothing methods have been findingtheir ways into everyday data analysis by practitioners. While scoresof methods have proved successful for univariate smoothing, onespractical in multivariate settings number far less. Smoothing splineANOVA models are a versatile family of smoothing methods derivedthrough roughness penalties, that are suitable for both univariate andmultivariate problems.In this book, the author presents a treatise on penalty smoothingunder a unified framework. Methods are developed for (i) regressionwith Gaussian and non-Gaussian responses as well as with censored lifetime data; (ii) density and conditional density estimation under avariety of sampling schemes; and (iii) hazard rate estimation withcensored life time data and covariates. The unifying themes are thegeneral penalized likelihood method and the construction ofmultivariate models with built-in ANOVA decompositions. Extensivediscussions are devoted to model construction, smoothing parameterselection, computation, and asymptotic convergence.Most of the computational and data analytical tools discussed in thebook are implemented in R, an open-source platform for statisticalcomputing and graphics. Suites of functions are embodied in the Rpackage gss, and are illustrated throughout the book using simulatedand real data examples.This monograph will be useful as a reference work for researchers intheoretical and applied statistics as well as for those in otherrelated disciplines. It can also be used as a text for graduate levelcourses on the subject. Most of the materials are accessibleto asecond year graduate student with a good training in calculus andlinear algebra and working knowledge in basic statistical inferencessuch as linear models and maximum likelihood estimates. 452 pp. Englisch.

Gebunden. Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Covers latest research of smoothing methods in data analysisSecond edition is updated with latest computational methods, including the uses ofthe R package gssEmpirical studies are expanded, reorganized, and mostly rerun using the latest.

Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Covers latest research of smoothing methods in data analysisSecond edition is updated with latest computational methods, including the uses ofthe R package gssEmpirical studies are expanded, reorganized, and mostly rerun using the latest.

Zustand: New. Print on Demand pp. 452 13 Illus.

Zustand: New. PRINT ON DEMAND pp. 452.

Buch. Zustand: Neu. Smoothing Spline ANOVA Models | Chong Gu | Buch | xviii | Englisch | 2013 | Springer US | EAN 9781461453680 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.