Articles liés à Bayesian Survival Analysis

Bayesian Survival Analysis - Couverture souple

 
9781441929334: Bayesian Survival Analysis

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Survival analysis arises in many fields of study including medicine, biology, engineering, public health, epidemiology, and economics. This book provides a comprehensive treatment of Bayesian survivial analysis and serves as a useful reference book for applied or theoretical researchers as well as practitioners.

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9780387952772: Bayesian Survival Analysis

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ISBN 10 :  0387952772 ISBN 13 :  9780387952772
Editeur : Springer-Verlag New York Inc., 2001
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Ibrahim, Joseph G. G.; Chen, Ming-Hui; Sinha, Debajyoti
Edité par Springer, 2010
ISBN 10 : 1441929339 ISBN 13 : 9781441929334
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Ibrahim, Joseph G. G.; Chen, Ming-Hui; Sinha, Debajyoti
Edité par Springer, 2010
ISBN 10 : 1441929339 ISBN 13 : 9781441929334
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Joseph G. Ibrahim
ISBN 10 : 1441929339 ISBN 13 : 9781441929334
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Paperback. Etat : new. Paperback. Survival analysis arises in many fields of study including medicine, biology, engineering, public health, epidemiology, and economics. This book provides a comprehensive treatment of Bayesian survival analysis.Several topics are addressed, including parametric models, semiparametric models based on prior processes, proportional and non-proportional hazards models, frailty models, cure rate models, model selection and comparison, joint models for longitudinal and survival data, models with time varying covariates, missing covariate data, design and monitoring of clinical trials, accelerated failure time models, models for mulitivariate survival data, and special types of hierarchial survival models. Also various censoring schemes are examined including right and interval censored data. Several additional topics are discussed, including noninformative and informative prior specificiations, computing posterior qualities of interest, Bayesian hypothesis testing, variable selection, model selection with nonnested models, model checking techniques using Bayesian diagnostic methods, and Markov chain Monte Carlo (MCMC) algorithms for sampling from the posteiror and predictive distributions.The book presents a balance between theory and applications, and for each class of models discussed, detailed examples and analyses from case studies are presented whenever possible. The applications are all essentially from the health sciences, including cancer, AIDS, and the environment. The book is intended as a graduate textbook or a reference book for a one semester course at the advanced masters or Ph.D. level. This book would be most suitable for second or third year graduate students in statistics or biostatistics. It would also serve as a useful reference book for applied or theoretical researchers as well as practitioners. Survival analysis arises in many fields of study including medicine, biology, engineering, public health, epidemiology, and economics. It presents a balance between theory and applications, and for each class of models discussed, detailed examples and analyses from case studies are presented whenever possible. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9781441929334

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Joseph G. Ibrahim|Ming-Hui Chen|Debajyoti Sinha
Edité par Springer New York, 2010
ISBN 10 : 1441929339 ISBN 13 : 9781441929334
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. First book on survival analysis from a Bayesian viewpointBalanced presentation of theory and apllicationsApplications taken from the health sciences, including AIDS, cancer, and design and monitoring of randomized clinical trialsSurvival analysi. N° de réf. du vendeur 4173402

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Joseph G. Ibrahim
Edité par Springer New York Dez 2010, 2010
ISBN 10 : 1441929339 ISBN 13 : 9781441929334
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Survival analysis arises in many fields of study including medicine, biology, engineering, public health, epidemiology, and economics. This book provides a comprehensive treatment of Bayesian survival analysis.Several topics are addressed, including parametric models, semiparametric models based on prior processes, proportional and non-proportional hazards models, frailty models, cure rate models, model selection and comparison, joint models for longitudinal and survival data, models with time varying covariates, missing covariate data, design and monitoring of clinical trials, accelerated failure time models, models for mulitivariate survival data, and special types of hierarchial survival models. Also various censoring schemes are examined including right and interval censored data. Several additional topics are discussed, including noninformative and informative prior specificiations, computing posterior qualities of interest, Bayesian hypothesis testing, variable selection, model selection with nonnested models, model checking techniques using Bayesian diagnostic methods, and Markov chain Monte Carlo (MCMC) algorithms for sampling from the posteiror and predictive distributions.The book presents a balance between theory and applications, and for each class of models discussed, detailed examples and analyses from case studies are presented whenever possible. The applications are all essentially from the health sciences, including cancer, AIDS, and the environment. The book is intended as a graduate textbook or a reference book for a one semester course at the advanced masters or Ph.D. level. This book would be most suitable for second or third year graduate students in statistics or biostatistics. It would also serve as a useful reference book for applied or theoretical researchers as well as practitioners. 496 pp. Englisch. N° de réf. du vendeur 9781441929334

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Joseph G. Ibrahim
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Taschenbuch. Etat : Neu. Neuware -Survival analysis arises in many fields of study including medicine, biology, engineering, public health, epidemiology, and economics. This book provides a comprehensive treatment of Bayesian survival analysis.Several topics are addressed, including parametric models, semiparametric models based on prior processes, proportional and non-proportional hazards models, frailty models, cure rate models, model selection and comparison, joint models for longitudinal and survival data, models with time varying covariates, missing covariate data, design and monitoring of clinical trials, accelerated failure time models, models for mulitivariate survival data, and special types of hierarchial survival models. Also various censoring schemes are examined including right and interval censored data. Several additional topics are discussed, including noninformative and informative prior specificiations, computing posterior qualities of interest, Bayesian hypothesis testing, variable selection, model selection with nonnested models, model checking techniques using Bayesian diagnostic methods, and Markov chain Monte Carlo (MCMC) algorithms for sampling from the posteiror and predictive distributions.The book presents a balance between theory and applications, and for each class of models discussed, detailed examples and analyses from case studies are presented whenever possible. The applications are all essentially from the health sciences, including cancer, AIDS, and the environment. The book is intended as a graduate textbook or a reference book for a one semester course at the advanced masters or Ph.D. level. This book would be most suitable for second or third year graduate students in statistics or biostatistics. It would also serve as a useful reference book for applied or theoretical researchers as well as practitioners.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 496 pp. Englisch. N° de réf. du vendeur 9781441929334

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Joseph G. Ibrahim Ming-Hui Chen Debajyoti Sinha
Edité par Springer, 2010
ISBN 10 : 1441929339 ISBN 13 : 9781441929334
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Joseph G. Ibrahim
Edité par Springer New York, 2010
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Survival analysis arises in many fields of study including medicine, biology, engineering, public health, epidemiology, and economics. This book provides a comprehensive treatment of Bayesian survival analysis.Several topics are addressed, including parametric models, semiparametric models based on prior processes, proportional and non-proportional hazards models, frailty models, cure rate models, model selection and comparison, joint models for longitudinal and survival data, models with time varying covariates, missing covariate data, design and monitoring of clinical trials, accelerated failure time models, models for mulitivariate survival data, and special types of hierarchial survival models. Also various censoring schemes are examined including right and interval censored data. Several additional topics are discussed, including noninformative and informative prior specificiations, computing posterior qualities of interest, Bayesian hypothesis testing, variable selection, model selection with nonnested models, model checking techniques using Bayesian diagnostic methods, and Markov chain Monte Carlo (MCMC) algorithms for sampling from the posteiror and predictive distributions.The book presents a balance between theory and applications, and for each class of models discussed, detailed examples and analyses from case studies are presented whenever possible. The applications are all essentially from the health sciences, including cancer, AIDS, and the environment. The book is intended as a graduate textbook or a reference book for a one semester course at the advanced masters or Ph.D. level. This book would be most suitable for second or third year graduate students in statistics or biostatistics. It would also serve as a useful reference book for applied or theoretical researchers as well as practitioners. N° de réf. du vendeur 9781441929334

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Ibrahim Joseph G. Chen Ming-Hui Sinha Debajyoti
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Ibrahim Joseph G. Chen Ming-Hui Sinha Debajyoti
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ISBN 10 : 1441929339 ISBN 13 : 9781441929334
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