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Bayesian hazard modeling based on lifetime data with latent heterogeneity

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  • Li, Mingyang
  • Liu, Jian

Abstract

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.

Suggested Citation

  • Li, Mingyang & Liu, Jian, 2016. "Bayesian hazard modeling based on lifetime data with latent heterogeneity," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 183-189.
    • Handle: RePEc:eee:reensy:v:145:y:2016:i:c:p:183-189
    DOI: 10.1016/j.ress.2015.09.007
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    References listed on IDEAS

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    1. Kaushik Patra & Dipak Dey, 2002. "A General Class of Change Point and Change Curve Modeling for Life Time Data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 54(3), pages 517-530, September.
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    Cited by:

    1. Altun, Mustafa & Comert, Salih Vehbi, 2016. "A change-point based reliability prediction model using field return data," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 175-184.
    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. 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.
    4. Zhang, Jian-Xun & Hu, Chang-Hua & He, Xiao & Si, Xiao-Sheng & Liu, Yang & Zhou, Dong-Hua, 2017. "Lifetime prognostics for deteriorating systems with time-varying random jumps," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 338-350.
    5. Lin, Kunsong & Chen, Yunxia & Xu, Dan, 2017. "Reliability assessment model considering heterogeneous population in a multiple stresses accelerated test," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 134-143.
    6. Hao Zeng & Xuxue Sun & Kuo Wang & Yuxin Wen & Wujun Si & Mingyang Li, 2024. "A Bayesian Approach for Lifetime Modeling and Prediction with Multi-Type Group-Shared Missing Covariates," Mathematics, MDPI, vol. 12(5), pages 1-23, February.
    7. Rezgar Zaki & Abbas Barabadi & Javad Barabady & Ali Nouri Qarahasanlou, 2022. "Observed and unobserved heterogeneity in failure data analysis," Journal of Risk and Reliability, , vol. 236(1), pages 194-207, February.
    8. 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.
    9. Reza Barabadi & Mohammad Ataei & Reza Khalokakaie & Ali Nouri Qarahasanlou, 2021. "Spare-part management in a heterogeneous environment," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-14, March.
    10. Sun, Xuxue & Mraied, Hesham & Cai, Wenjun & Zhang, Qiong & Liang, Guoyuan & Li, Mingyang, 2018. "Bayesian latent degradation performance modeling and quantification of corroding aluminum alloys," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 84-96.

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