Bayesian hazard modeling based on lifetime data with latent heterogeneity

Lifetime data collected from reliability tests or field operations often exhibit significant heterogeneity patterns caused by latent factors. Such latent heterogeneity indicates that lifetime observations may belong to different sub-populations with different distribution parameters. As a result, the assumption on data homogeneity adopted by conventional reliability modeling techniques becomes inappropriate. Effective identification and quantification of such heterogeneity is crucial for more reliable model estimation and subsequent optimal decision making in a variety of reliability assurance activities. This research proposes a full Bayesian modeling framework for statistical hazard modeling of latent heterogeneity in lifetime data. The proposed framework is generic and comprehensive by systematically addressing different modeling aspects, which include modeling sub-populations with different hazard rates changing over time and different responses to the same stress factors, determining the number of sub-populations, identifying the most appropriate sub-population model structures, estimating model parameters and performing predictive inference. A numerical case study demonstrates the validity and effectiveness of the proposed approach.