A Two-Stage Failure Model for Bayesian Change Point Analysis

This paper presents a new approach for detecting certain change-points, which may disturb the evaluation of reliability models with covariates, via a two-stage failure model, and stochastic time-lagged regression functions. The proposed model is developed with the Bayesian survival analysis method, and thus the problems for censored (or truncated) data in reliability tests can be resolved. In addition, a Markov chain Monte Carlo method based on Gibbs sampling is used to dynamically simulate the Markov chain of the parameterspsila posterior distribution. Finally, a numeric example is discussed to demonstrate the proposed model.