Variational Bayes for analysis of masked data - Rai, Himanshu; Tomer, Sanjeev K. (Journal of computational science, 2025)
Bayesian competing risks analysis in presence of masked data often leads to an intractable posterior, for which Markov chain Monte Carlo (MCMC) methods are frequently utilized to evaluate various estimators of interest. However, while analyzing several risks simultaneously, MCMC methods may consume substantial amount of computation time. This paper introduces Variational Bayes, a machine learning technique, as an efficient alternative to MCMC for Bayesian analysis of competing risk data. Variational Bayes demonstrates faster convergence than MCMC, particularly in the context of extensive competing risk datasets. We compare the performance of variational Bayes over Gibbs sampling with respect to the number of considered risks through a simulation study. Additionally, we apply the two methods to analyze a real data set of computer hard drives. •Analysis of competing risk model with missing cause of failure.•Implementation of variational Bayes as an alternative to MCMC for the analysis of masked data.•Comparison of computational time taken by variational Bayes and MCMC algorithm.•Fitting of competing risk data using the hazard plot.