Bayesian Inference and Computation in Reliability and Survival Analysis (Emerging Topics in Statistics and Biostatistics) - Hardcover

Hardcover

ISBN 10: 3030886573 ISBN 13: 9783030886578

Publisher: Springer, 2022

Synopsis

Bayesian analysis is one of the important tools for statistical modelling and inference. Bayesian frameworks and methods have been successfully applied to solve practical problems in reliability and survival analysis, which have a wide range of real world applications in medical and biological sciences, social and economic sciences, and engineering. In the past few decades, significant developments of Bayesian inference have been made by many researchers, and advancements in computational technology and computer performance has laid the groundwork for new opportunities in Bayesian computation for practitioners.

Because these theoretical and technological developments introduce new questions and challenges, and increase the complexity of the Bayesian framework, this book brings together experts engaged in groundbreaking research on Bayesian inference and computation to discuss important issues, with emphasis on applications to reliability and survival analysis. Topics covered are timely and have the potential to influence the interacting worlds of biostatistics, engineering, medical sciences, statistics, and more.

The included chapters present current methods, theories, and applications in the diverse area of biostatistical analysis. The volume as a whole serves as reference in driving quality global health research.  

About this title

About the Author

Dr. Yuhlong Lio is a professor with the Department of Mathematical Sciences at the University of South Dakota, Vermillion, SD, USA. He is an associate editor of professional journals, including the Journal of Statistical Computation and Simulation. He is the co-editor of Statistical Modeling for Degradation Data and Statistical Quality Technologies: Theory and Practice. His research interests include reliability, quality control, censoring methodology, kernel smoothing estimation, and accelerated degradation data modelling. Dr. Lio has more than 100 refereed publications.

Dr. Ding-Geng Chen is a fellow of the American Statistical Association and currently the executive director and professor in biostatistics at Arizona State University. He was the Wallace Kuralt Distinguished Professor at the University of North Carolina at Chapel Hill, a professor in biostatistics at the University of Rochester, and the Karl E. Peace Endowed Eminent Scholar Chair in Biostatistics at Georgia Southern University. He is also a senior statistics consultant for biopharmaceuticals and government agencies with extensive expertise in Monte Carlo simulations, clinical trial biostatistics, and public health statistics. Dr. Chen has more than 200 refereed professional publications, and he has co-authored and co-edited 33 books on clinical trial methodology, meta-analysis, and public health applications. He has been invited nationally and internationally to give speeches on his research. Dr. Chen was honored with the "Award of Recognition" in 2014 by the Deming Conference Committee for highly successful advanced biostatistics workshop tutorials with his books.

Dr. Hon Keung Tony Ng is currently a professor of statistical science with Southern Methodist University, Dallas, TX, USA. He is an associate editor of Communications in Statistics, Computational Statistics, IEEE Transactions on Reliability,Journal of Statistical Computation and Simulation, Naval Research Logistics, Sequential Analysis and Statistics and Probability Letters. His research interests include reliability, censoring methodology, ordered data analysis, nonparametric methods, and statistical inference. He has published more than 140 research papers in refereed journals. He is the co-author of the book Precedence-Type Tests and Applications and co-editor of Statistical Modeling for Degradation Data, Statistical Quality Technologies: Theory and Practice, Ordered Data Analysis, and Modeling and Health Research Methods. Professor Ng is a fellow of the American Statistical Association, an elected senior member of IEEE, and an elected member of the International Statistical Institute.

Dr. Tzong-Ru Tsai is currently the Dean of the College of Business and Management and a professor in the Department of Statistics at Tamkang University inNew Taipei City, Taiwan. His main research interests include quality control, reliability analysis, and machine learning. He has served as a consultant with extensive expertise in statistical quality control and experimental design for many companies in the past years. He is the co-editor of Statistical Modeling for Degradation Data and Statistical Quality Technologies: Theory and Practice. He is an associate editor of the Journal of Statistical Computation and Simulation and Mathematics. Dr. Tsai has more than 100 refereed publications.

From the Back Cover

Bayesian analysis is one of the important tools for statistical modelling and inference. Bayesian frameworks and methods have been successfully applied to solve practical problems in reliability and survival analysis, which have a wide range of real world applications in medical and biological sciences, social and economic sciences, and engineering. In the past few decades, significant developments of Bayesian inference have been made by many researchers, and advancements in computational technology and computer performance has laid the groundwork for new opportunities in Bayesian computation for practitioners.

Because these theoretical and technological developments introduce new questions and challenges, and increase the complexity of the Bayesian framework, this book brings together experts engaged in groundbreaking research on Bayesian inference and computation to discuss important issues, with emphasis on applications to reliability and survival analysis. Topics covered are timely and have the potential to influence the interacting worlds of biostatistics, engineering, medical sciences, statistics, and more.

The included chapters present current methods, theories, and applications in the diverse area of biostatistical analysis. The volume as a whole serves as reference in driving quality global health research.  

Edition details

• Publisher: Springer
• Publication date: 2022
• Language: English
• ISBN 10: 3030886573
• ISBN 13: 9783030886578
• Binding: Hardcover
• Edition number: 1
• Number of pages: 382
• Editor: Lio Yuhlong, Chen Ding-Geng, Ng Hon Keung Tony, Tsai Tzong-Ru