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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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    1. Omiros Papaspiliopoulos & Gareth O. Roberts, 2008. "Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models," Biometrika, Biometrika Trust, vol. 95(1), pages 169-186.
    2. Zhengxin Zhang & Xiaosheng Si & Changhua Hu & Xiangyu Kong, 2015. "Degradation modeling–based remaining useful life estimation: A review on approaches for systems with heterogeneity," Journal of Risk and Reliability, , vol. 229(4), pages 343-355, August.
    3. Anne B. Koehler & Emily S. Murphree, 1988. "A Comparison of the Akaike and Schwarz Criteria for Selecting Model Order," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 37(2), pages 187-195, June.
    4. Tao Yuan & Xiaoyan Zhu, 2012. "Reliability study of ultra-thin dielectric films with variable thickness levels," IISE Transactions, Taylor & Francis Journals, vol. 44(9), pages 744-753.
    5. Mingyang Li & Jiali Han & Jian Liu, 2017. "Bayesian nonparametric modeling of heterogeneous time-to-event data with an unknown number of sub-populations," IISE Transactions, Taylor & Francis Journals, vol. 49(5), pages 481-492, May.
    6. Peter J. Green & Sylvia Richardson, 2001. "Modelling Heterogeneity With and Without the Dirichlet Process," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 28(2), pages 355-375, June.
    7. Sylvia. Richardson & Peter J. Green, 1997. "On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(4), pages 731-792.
    8. 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.
    9. Maria De Iorio & Wesley O. Johnson & Peter Müller & Gary L. Rosner, 2009. "Bayesian Nonparametric Nonproportional Hazards Survival Modeling," Biometrics, The International Biometric Society, vol. 65(3), pages 762-771, September.
    10. Raed Kontar & Junbo Son & Shiyu Zhou & Chaitanya Sankavaram & Yilu Zhang & Xinyu Du, 2017. "Remaining useful life prediction based on the mixed effects model with mixture prior distribution," IISE Transactions, Taylor & Francis Journals, vol. 49(7), pages 682-697, July.
    11. Richard L Warr & David H Collins, 2014. "Bayesian nonparametric models for combining heterogeneous reliability data," Journal of Risk and Reliability, , vol. 228(2), pages 166-175, April.
    12. Castet, Jean-Francois & Saleh, Joseph H., 2010. "Single versus mixture Weibull distributions for nonparametric satellite reliability," Reliability Engineering and System Safety, Elsevier, vol. 95(3), pages 295-300.
    13. Yisha Xiang & David Coit & Qianmei Feng, 2013. "Subpopulations experiencing stochastic degradation: reliability modeling, burn-in, and preventive replacement optimization," IISE Transactions, Taylor & Francis Journals, vol. 45(4), pages 391-408.
    14. Jiang, R. & Murthy, D.N.P., 2009. "Impact of quality variations on product reliability," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 490-496.
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    Cited by:

    1. Peña-Ramírez, Fernando A. & Guerra, Renata Rojas & Canterle, Diego Ramos & Cordeiro, Gauss M., 2020. "The logistic Nadarajah–Haghighi distribution and its associated regression model for reliability applications," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    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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