A nonparametric Bayesian modeling approach for heterogeneous lifetime data with covariates

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

13 Scopus Citations
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Original languageEnglish
Pages (from-to)95-104
Journal / PublicationReliability Engineering and System Safety
Online published23 May 2017
Publication statusPublished - Nov 2017


Lifetime data collected at product design and production stage or field operational stage often exhibit heterogeneity patterns, making the homogeneity assumption in conventional statistical lifetime models invalid. Mixture models are important modeling approaches that account for data heterogeneity. However, existing mixture models are constrained by assuming an known number of sub-populations. This paper proposes a new Bayesian statistical model to analyze heterogeneous lifetime data by assuming an unknown number of sub-populations. Each sub-population is characterized by an accelerated failure time model to quantify the effects of possible reliability impact factors. The proposed model allows simultaneous identification of the number of sub-populations and the model parameters of sub-populations. Convenient sampling strategies are further proposed to address the challenges of model estimation. Both numerical case study and real case study are provided to illustrate the proposed approach and demonstrate its validity.

Research Area(s)

  • Bayesian modeling, Gibbs sampler, Lifetime regression, Mixture model, Model selection