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Edité par Springer-Verlag New York Inc., New York, NY, 2010
ISBN 10 : 1441924566 ISBN 13 : 9781441924568
Langue: anglais
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Ajouter au panierPaperback. Etat : new. Paperback. The Akaike information criterion (AIC) derived as an estimator of the Kullback-Leibler information discrepancy provides a useful tool for evaluating statistical models, and numerous successful applications of the AIC have been reported in various fields of natural sciences, social sciences and engineering.One of the main objectives of this book is to provide comprehensive explanations of the concepts and derivations of the AIC and related criteria, including Schwarzs Bayesian information criterion (BIC), together with a wide range of practical examples of model selection and evaluation criteria. A secondary objective is to provide a theoretical basis for the analysis and extension of information criteria via a statistical functional approach. A generalized information criterion (GIC) and a bootstrap information criterion are presented, which provide unified tools for modeling and model evaluation for a diverse range of models, including various types of nonlinear models and model estimation procedures such as robust estimation, the maximum penalized likelihood method and a Bayesian approach. This brilliantly structured and comprehensive volume provides exhaustive explanations of the concepts and philosophy of statistical modeling, together with a wide range of practical and numerical examples. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Edité par Springer-Verlag New York Inc., New York, NY, 2007
ISBN 10 : 0387718869 ISBN 13 : 9780387718866
Langue: anglais
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Ajouter au panierHardcover. Etat : new. Hardcover. The Akaike information criterion (AIC) derived as an estimator of the Kullback-Leibler information discrepancy provides a useful tool for evaluating statistical models, and numerous successful applications of the AIC have been reported in various fields of natural sciences, social sciences and engineering.One of the main objectives of this book is to provide comprehensive explanations of the concepts and derivations of the AIC and related criteria, including Schwarzs Bayesian information criterion (BIC), together with a wide range of practical examples of model selection and evaluation criteria. A secondary objective is to provide a theoretical basis for the analysis and extension of information criteria via a statistical functional approach. A generalized information criterion (GIC) and a bootstrap information criterion are presented, which provide unified tools for modeling and model evaluation for a diverse range of models, including various types of nonlinear models and model estimation procedures such as robust estimation, the maximum penalized likelihood method and a Bayesian approach. This brilliantly structured and comprehensive volume provides exhaustive explanations of the concepts and philosophy of statistical modeling, together with a wide range of practical and numerical examples. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Edité par Springer-Verlag New York Inc., New York, NY, 2010
ISBN 10 : 1441924566 ISBN 13 : 9781441924568
Langue: anglais
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Ajouter au panierPaperback. Etat : new. Paperback. The Akaike information criterion (AIC) derived as an estimator of the Kullback-Leibler information discrepancy provides a useful tool for evaluating statistical models, and numerous successful applications of the AIC have been reported in various fields of natural sciences, social sciences and engineering.One of the main objectives of this book is to provide comprehensive explanations of the concepts and derivations of the AIC and related criteria, including Schwarzs Bayesian information criterion (BIC), together with a wide range of practical examples of model selection and evaluation criteria. A secondary objective is to provide a theoretical basis for the analysis and extension of information criteria via a statistical functional approach. A generalized information criterion (GIC) and a bootstrap information criterion are presented, which provide unified tools for modeling and model evaluation for a diverse range of models, including various types of nonlinear models and model estimation procedures such as robust estimation, the maximum penalized likelihood method and a Bayesian approach. This brilliantly structured and comprehensive volume provides exhaustive explanations of the concepts and philosophy of statistical modeling, together with a wide range of practical and numerical examples. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Ajouter au panierHardcover. Etat : Brand New. 1st edition. 273 pages. 9.25x6.25x0.50 inches. In Stock.
Edité par Springer-Verlag New York Inc., New York, NY, 2007
ISBN 10 : 0387718869 ISBN 13 : 9780387718866
Langue: anglais
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Ajouter au panierHardcover. Etat : new. Hardcover. The Akaike information criterion (AIC) derived as an estimator of the Kullback-Leibler information discrepancy provides a useful tool for evaluating statistical models, and numerous successful applications of the AIC have been reported in various fields of natural sciences, social sciences and engineering.One of the main objectives of this book is to provide comprehensive explanations of the concepts and derivations of the AIC and related criteria, including Schwarzs Bayesian information criterion (BIC), together with a wide range of practical examples of model selection and evaluation criteria. A secondary objective is to provide a theoretical basis for the analysis and extension of information criteria via a statistical functional approach. A generalized information criterion (GIC) and a bootstrap information criterion are presented, which provide unified tools for modeling and model evaluation for a diverse range of models, including various types of nonlinear models and model estimation procedures such as robust estimation, the maximum penalized likelihood method and a Bayesian approach. This brilliantly structured and comprehensive volume provides exhaustive explanations of the concepts and philosophy of statistical modeling, together with a wide range of practical and numerical examples. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Ajouter au panierEtat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. With the development of modeling techniques, it has been required to construct model selection criteria, relaxing the assumptions imposed AIC and BICStatistical modeling is a critical tool in scientific research. This book provides comprehensi.
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Ajouter au panierEtat : New. Print on Demand pp. 290 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam.
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Ajouter au panierEtat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. With the development of modeling techniques, it has been required to construct model selection criteria, relaxing the assumptions imposed AIC and BICStatistical modeling is a critical tool in scientific research. This book provides comprehensi.
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Ajouter au panierEtat : New. Print on Demand pp. 292 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam.
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Ajouter au panierEtat : New. PRINT ON DEMAND pp. 292.