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A nonparametric Bayesian modeling approach for heterogeneous lifetime data with covariates

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  • Li, Mingyang
  • Meng, Hongdao
  • Zhang, Qingpeng

Abstract

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.

Suggested Citation

  • Li, Mingyang & Meng, Hongdao & Zhang, Qingpeng, 2017. "A nonparametric Bayesian modeling approach for heterogeneous lifetime data with covariates," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 95-104.
    • Handle: RePEc:eee:reensy:v:167:y:2017:i:c:p:95-104
    DOI: 10.1016/j.ress.2017.05.029
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    Cited by:

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    2. Santos, Cristiano C. & Loschi, Rosangela H., 2020. "Semi-parametric Bayesian models for heterogeneous degradation data: An application to laser data," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    3. Zhou, Chongwen & Chinnam, Ratna Babu & Dalkiran, Evrim & Korostelev, Alexander, 2017. "Bayesian approach to hazard rate models for early detection of warranty and reliability problems using upstream supply chain information," International Journal of Production Economics, Elsevier, vol. 193(C), pages 316-331.
    4. Ducros, Florence & Pamphile, Patrick, 2018. "Bayesian estimation of Weibull mixture in heavily censored data setting," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 453-462.
    5. Yang, Lechang & Wang, Pidong & Wang, Qiang & Bi, Sifeng & Peng, Rui & Behrensdorf, Jasper & Beer, Michael, 2021. "Reliability analysis of a complex system with hybrid structures and multi-level dependent life metrics," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    6. Kowal, Karol, 2022. "Lifetime reliability and availability simulation for the electrical system of HTTR coupled to the electricity-hydrogen cogeneration plant," Reliability Engineering and System Safety, Elsevier, vol. 223(C).

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