Bayesian Inference and Computation in Reliability and Survival Analysis

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.

Features the latest developments in Bayesian statistical inference Provides a necessary background of mathematical and statistical models Explores the practical applicability of novel Bayesian statistical techniques

Auteur

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 in New 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.

Contenu

1. A Bayesian Approach for Step-stress Accelerated Life-tests for One-shot Devices under Exponential Distributions.- 2. Bayesian Estimation of Stress-strength Parameter for Moran-Downton Bivariate Exponential Distribution under Progressive Type-II Censoring.- 3. Bayesian Computation in A Birnbaum-Saunders Reliability Model with Applications to Fatigue Data.- 4. A Competing Risks Model Based on A Two-parameter Exponential Family Distribution under Progressive Type-II Censoring.- 5. Bayesian Computations for Reliability Analysis in Dynamic Environments.- 6. Bayesian Analysis of Stochastic Processes in Reliability.- 7. Bayesian Analysis of A New Bivariate Wiener Degradation Process.- 8. Bayesian Estimation for Bivariate Gamma Processes with Copula.- 9. Review of Statistical Treatment for Oncology Dose Escalation Trial with Prolonged Evaluation Window or Fast Enrollment.- 10. A Bayesian Approach for the Analysis of Tumorigenicity Data from Sacrificial Experiments under Weibull Lifetimes.- 11. Bayesian Sensitivity Analysis in Survival and Longitudinal Trial with Missing Data.- 12. Bayesian Analysis for Clustered Data under A Semi-competing Risks Framework.- 13. Survival Analysis for the Inverse Gaussian Distribution: Natural Conjugate and Jeffrey's Priors.- 14. Bayesian Inferences for Panel Count Data and Interval-censored Data with Nonparametric Modeling of the Baseline Functions.- 15. Bayesian Approach for Interval-censored Survival Data with Time-varying Coefficients.- 16. Bayesian Approach for Joint-modeling Longitudinal Data and Survival Data Simultaneously in Public Health Studies <p