Joseph G. Ibrahim – författare

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13 produkter

Handbook of Survival Analysis
AvJohn P. Klein,Hans C. van Houwelingenm. fl.
Häftad, Engelska, 2020
1 190 kr
Skickas inom 10-15 vardagar
Handbook of Survival Analysis presents modern techniques and research problems in lifetime data analysis. This area of statistics deals with time-to-event data that is complicated by censoring and the dynamic nature of events occurring in time.With chapters written by leading researchers in the field, the handbook focuses on advances in survival analysis techniques, covering classical and Bayesian approaches. It gives a complete overview of the current status of survival analysis and should inspire further research in the field. Accessible to a wide range of readers, the book provides:An introduction to various areas in survival analysis for graduate students and novicesA reference to modern investigations into survival analysis for more established researchers A text or supplement for a second or advanced course in survival analysisA useful guide to statistical methods for analyzing survival data experiments for practicing statisticians
Bayesian Survival Analysis
AvJoseph G. Ibrahim,Ming-Hui Chenm. fl.
Inbunden, Engelska, 2001
2 220 kr
Skickas inom 10-15 vardagar
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.
Monte Carlo Methods in Bayesian Computation
AvMing-Hui Chen,Qi-Man Shaom. fl.
Inbunden, Engelska, 2000
1 113 kr
Skickas inom 10-15 vardagar
Sampling from the posterior distribution and computing posterior quanti ties of interest using Markov chain Monte Carlo (MCMC) samples are two major challenges involved in advanced Bayesian computation. This book examines each of these issues in detail and focuses heavily on comput ing various posterior quantities of interest from a given MCMC sample. Several topics are addressed, including techniques for MCMC sampling, Monte Carlo (MC) methods for estimation of posterior summaries, improv ing simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, Highest Poste rior Density (HPD) interval calculations, computation of posterior modes, and posterior computations for proportional hazards models and Dirichlet process models. Also extensive discussion is given for computations in volving model comparisons, including both nested and nonnested models. Marginal likelihood methods, ratios of normalizing constants, Bayes fac tors, the Savage-Dickey density ratio, Stochastic Search Variable Selection (SSVS), Bayesian Model Averaging (BMA), the reverse jump algorithm, and model adequacy using predictive and latent residual approaches are also discussed. The book presents an equal mixture of theory and real applications.
Handbook of Survival Analysis
AvThomas H. Scheike,Joseph G. Ibrahimm. fl.
E-bok
Engelska, 2016
1 381 kr
Läs direkt efter köp
Handbook of Survival Analysis presents modern techniques and research problems in lifetime data analysis. This area of statistics deals with time-to-event data that is complicated by censoring and the dynamic nature of events occurring in time.
With chapters written by leading researchers in the field, the handbook focuses on advances in survival analysis techniques, covering classical and Bayesian approaches. It gives a complete overview of the current status of survival analysis and should inspire further research in the field. Accessible to a wide range of readers, the book provides:An introduction to various areas in survival analysis for graduate students and novicesA reference to modern investigations into survival analysis for more established researchers A text or supplement for a second or advanced course in survival analysisA useful guide to statistical methods for analyzing survival data experiments for practicing statisticians
Bayesian Survival Analysis
AvJoseph G. Ibrahim,Ming-Hui Chenm. fl.
Häftad, Engelska, 2010
2 220 kr
Skickas inom 10-15 vardagar
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.
Monte Carlo Methods in Bayesian Computation
AvJoseph G. Ibrahim,Qi-Man Shaom. fl.
E-bok
PDF, Engelska, 2012
1 413 kr
Läs direkt efter köp
Sampling from the posterior distribution and computing posterior quanti ties of interest using Markov chain Monte Carlo (MCMC) samples are two major challenges involved in advanced Bayesian computation. This book examines each of these issues in detail and focuses heavily on comput ing various posterior quantities of interest from a given MCMC sample. Several topics are addressed, including techniques for MCMC sampling, Monte Carlo (MC) methods for estimation of posterior summaries, improv ing simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, Highest Poste rior Density (HPD) interval calculations, computation of posterior modes, and posterior computations for proportional hazards models and Dirichlet process models. Also extensive discussion is given for computations in volving model comparisons, including both nested and nonnested models. Marginal likelihood methods, ratios of normalizing constants, Bayes fac tors, the Savage-Dickey density ratio, Stochastic Search Variable Selection (SSVS), Bayesian Model Averaging (BMA), the reverse jump algorithm, and model adequacy using predictive and latent residual approaches are also discussed. The book presents an equal mixture of theory and real applications.
Monte Carlo Methods in Bayesian Computation
AvMing-Hui Chen,Qi-Man Shaom. fl.
Häftad, Engelska, 2021
426 kr
Skickas inom 5-8 vardagar
Monte Carlo Methods in Bayesian Computation
AvMing-Hui Chen,Qi-Man Shaom. fl.
Häftad, Engelska, 2012
1 113 kr
Skickas inom 10-15 vardagar
Sampling from the posterior distribution and computing posterior quanti ties of interest using Markov chain Monte Carlo (MCMC) samples are two major challenges involved in advanced Bayesian computation. This book examines each of these issues in detail and focuses heavily on comput ing various posterior quantities of interest from a given MCMC sample. Several topics are addressed, including techniques for MCMC sampling, Monte Carlo (MC) methods for estimation of posterior summaries, improv ing simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, Highest Poste rior Density (HPD) interval calculations, computation of posterior modes, and posterior computations for proportional hazards models and Dirichlet process models. Also extensive discussion is given for computations in volving model comparisons, including both nested and nonnested models. Marginal likelihood methods, ratios of normalizing constants, Bayes fac tors, the Savage-Dickey density ratio, Stochastic Search Variable Selection (SSVS), Bayesian Model Averaging (BMA), the reverse jump algorithm, and model adequacy using predictive and latent residual approaches are also discussed. The book presents an equal mixture of theory and real applications.
Handbook of Survival Analysis
AvJohn P. Klein,Hans C. van Houwelingenm. fl.
Inbunden, Engelska, 2013
4,0 utav 5 stjärnor. Totalt antal röster:(1)
3 098 kr
Skickas inom 10-15 vardagar
Handbook of Survival Analysis presents modern techniques and research problems in lifetime data analysis. This area of statistics deals with time-to-event data that is complicated by censoring and the dynamic nature of events occurring in time.With chapters written by leading researchers in the field, the handbook focuses on advances in survival analysis techniques, covering classical and Bayesian approaches. It gives a complete overview of the current status of survival analysis and should inspire further research in the field. Accessible to a wide range of readers, the book provides:An introduction to various areas in survival analysis for graduate students and novicesA reference to modern investigations into survival analysis for more established researchers A text or supplement for a second or advanced course in survival analysisA useful guide to statistical methods for analyzing survival data experiments for practicing statisticians
Handbook of Survival Analysis
AvThomas H. Scheike,Joseph G. Ibrahimm. fl.
E-bok
PDF, Engelska, 2016
1 396 kr
Läs direkt efter köp
Handbook of Survival Analysis presents modern techniques and research problems in lifetime data analysis. This area of statistics deals with time-to-event data that is complicated by censoring and the dynamic nature of events occurring in time.
With chapters written by leading researchers in the field, the handbook focuses on advances in survival analysis techniques, covering classical and Bayesian approaches. It gives a complete overview of the current status of survival analysis and should inspire further research in the field. Accessible to a wide range of readers, the book provides:An introduction to various areas in survival analysis for graduate students and novicesA reference to modern investigations into survival analysis for more established researchers A text or supplement for a second or advanced course in survival analysisA useful guide to statistical methods for analyzing survival data experiments for practicing statisticians
Practical Bayesian Analysis Using SAS
AvJoseph G. Ibrahim,Fang K. Chen
Inbunden, Engelska
1 081 kr
Kommande
Bayesian Survival Analysis
AvDebajyoti Sinha,Ming-Hui Chenm. fl.
E-bok
PDF, Engelska, 2013
2 833 kr
Läs direkt efter köp
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.
Bayesian Survival Analysis
AvJoseph G. Ibrahim,Ming-Hui Chen
Engelska, 2013
672 kr
Skickas inom 5-8 vardagar

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Joseph G. Ibrahim – författare

Handbook of Survival Analysis

Bayesian Survival Analysis

Monte Carlo Methods in Bayesian Computation

Handbook of Survival Analysis

Bayesian Survival Analysis

Monte Carlo Methods in Bayesian Computation

Monte Carlo Methods in Bayesian Computation

Monte Carlo Methods in Bayesian Computation

Handbook of Survival Analysis

Handbook of Survival Analysis

Practical Bayesian Analysis Using SAS

Bayesian Survival Analysis

Bayesian Survival Analysis

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