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Publisher’s description: 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.
The articles of this volume will be reviewed individually.
Indexed articles:
Ling, Man Ho; Hu, Xuwen, A Bayesian approach for step-stress-accelerated life tests for one-shot devices under exponential distributions, 3-16 [Zbl 07619950]
Lin, Yu-Jau; Lio, Yuhlong; Ng, Hon Keung Tony; Wang, Liang, Bayesian estimation of stress-strength parameter for Moran-Downton bivariate exponential distribution under progressive type II censoring, 17-40 [Zbl 07619951]
Leiva, Víctor; Ruggeri, Fabrizio; Laniado, Henry, Bayesian computation in a Birnbaum-Saunders reliability model with applications to fatigue data, 41-55 [Zbl 07619952]
Lin, Yu-Jau; Tsai, Tzong-Ru; Chen, Ding-Geng; Lio, Yuhlong, A competing risk model based on a two-parameter exponential family distribution under progressive type II censoring, 57-97 [Zbl 07619953]
Ay, Atilla; Soyer, Refik, Bayesian computations for reliability analysis in dynamic environments, 101-120 [Zbl 07619955]
Gouno, Evans, Bayesian analysis of stochastic processes in reliability, 121-146 [Zbl 07619956]
Xu, Ancha, Bayesian analysis of a new bivariate Wiener degradation process, 147-167 [Zbl 07619957]
Lin, Yu-Jau; Tsai, Tzong-Ru; Lio, Yuhlong, Bayesian estimation for bivariate gamma processes with copula, 169-187 [Zbl 07619958]
Wei, Xin; Liu, Rong, Review of statistical treatment for oncology dose-escalation trial with prolonged evaluation window or fast enrollment, 191-213 [Zbl 07619960]
Ling, Man Ho; So, Hon Yiu; Balakrishnan, Narayanaswamy, A Bayesian approach for the analysis of tumorigenicity data from sacrificial experiments under Weibull lifetimes, 215-237 [Zbl 07619961]
Liu, G. Frank; Chen, Fang, Bayesian sensitivity analysis in survival and longitudinal trials with missing data, 239-259 [Zbl 07619962]
Kim, Seong W.; Hong, Sehwa; Han, Yewon; Kim, Jinheum, Bayesian analysis for clustered data under a semi-competing risks framework, 261-278 [Zbl 07619963]
Eifert, Erin P.; Jayalath, Kalanka P.; Chhikara, Raj S., Survival analysis for the inverse Gaussian distribution: natural conjugate and Jeffrey’s priors, 279-298 [Zbl 07619964]
Wang, Lu; Wang, Lianming; Lin, Xiaoyan, Bayesian inferences for panel count data and interval-censored data with nonparametric modeling of the baseline functions, 299-321 [Zbl 07619965]
Zhang, Yue; Zhang, Bin, Bayesian approach for interval-censored survival data with time-varying coefficients, 323-341 [Zbl 07619966]
Chen, Ding-Geng; Lio, Yuhlong; Wilson, Jeffrey R., Bayesian approach for joint modeling longitudinal data and survival data simultaneously in public health studies, 343-355 [Zbl 07619967]