Bayesian Model Choice in Exponential Survival Models
ABSTRACT This article introduces four survival models: simple model, change point model, finite mixture model, and survival fraction model. Among these models, in order to choice the best model, we proposed a model choice method using approaches of Gelfand and Ghosh (1998). Then to avoid the computational difficulties, data augmentation method (Tanner and Wong, 1987) and Gibbs sampler (Gelfand and Smith, 1990) are employed. Our methodology is applied to simulated data and Stangl data On-impramint Hydrochloride data. The results indicate that our methodology is not sensitive for prior and weight of criterion.