IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v145y2016icp183-189.html
   My bibliography  Save this article

Bayesian hazard modeling based on lifetime data with latent heterogeneity

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832015002719
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2015.09.007?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    2. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    3. James Vaupel & Kenneth Manton & Eric Stallard, 1979. "The impact of heterogeneity in individual frailty on the dynamics of mortality," Demography, Springer;Population Association of America (PAA), vol. 16(3), pages 439-454, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Luping Zhao & Timothy E. Hanson, 2011. "Spatially Dependent Polya Tree Modeling for Survival Data," Biometrics, The International Biometric Society, vol. 67(2), pages 391-403, June.
    2. Vicente G. Cancho & Gladys D. C. Barriga & Gauss M. Cordeiro & Edwin M. M. Ortega & Adriano K. Suzuki, 2021. "Bayesian survival model induced by frailty for lifetime with long‐term survivors," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 75(3), pages 299-323, August.
    3. Maxim S. Finkelstein, 2003. "Modeling failure (mortality) rate with a change point," MPIDR Working Papers WP-2003-041, Max Planck Institute for Demographic Research, Rostock, Germany.
    4. Mojtaba Ganjali & T. Baghfalaki & D. Berridge, 2014. "A Bayesian Analysis of Unobserved Heterogeneity for Unemployment Duration Data in the Presence of Interval Censoring," International Econometric Review (IER), Econometric Research Association, vol. 6(1), pages 24-41, April.
    5. William J. Browne & Fiona Steele & Mousa Golalizadeh & Martin J. Green, 2009. "The use of simple reparameterizations to improve the efficiency of Markov chain Monte Carlo estimation for multilevel models with applications to discrete time survival models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(3), pages 579-598, June.
    6. Mitra Rahimzadeh & Ebrahim Hajizadeh & Farzad Eskandari, 2011. "Non-mixture cure correlated frailty models in Bayesian approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(8), pages 1651-1663, August.
    7. Buddhavarapu, Prasad & Bansal, Prateek & Prozzi, Jorge A., 2021. "A new spatial count data model with time-varying parameters," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 566-586.
    8. Mumtaz, Haroon & Theodoridis, Konstantinos, 2017. "Common and country specific economic uncertainty," Journal of International Economics, Elsevier, vol. 105(C), pages 205-216.
    9. Christina Leuker & Thorsten Pachur & Ralph Hertwig & Timothy J. Pleskac, 2019. "Do people exploit risk–reward structures to simplify information processing in risky choice?," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 5(1), pages 76-94, August.
    10. Bagdonavicius, Vilijandas & Nikulin, Mikhail, 2000. "On goodness-of-fit for the linear transformation and frailty models," Statistics & Probability Letters, Elsevier, vol. 47(2), pages 177-188, April.
    11. Rubio, F.J. & Steel, M.F.J., 2011. "Inference for grouped data with a truncated skew-Laplace distribution," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3218-3231, December.
    12. Alessandri, Piergiorgio & Mumtaz, Haroon, 2019. "Financial regimes and uncertainty shocks," Journal of Monetary Economics, Elsevier, vol. 101(C), pages 31-46.
    13. Feehan, Dennis & Wrigley-Field, Elizabeth, 2020. "How do populations aggregate?," SocArXiv 2fkw3, Center for Open Science.
    14. Svetlana V. Tishkovskaya & Paul G. Blackwell, 2021. "Bayesian estimation of heterogeneous environments from animal movement data," Environmetrics, John Wiley & Sons, Ltd., vol. 32(6), September.
    15. Leonardo Oliveira Martins & Hirohisa Kishino, 2010. "Distribution of distances between topologies and its effect on detection of phylogenetic recombination," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(1), pages 145-159, February.
    16. Tamal Ghosh & Malay Ghosh & Jerry J. Maples & Xueying Tang, 2022. "Multivariate Global-Local Priors for Small Area Estimation," Stats, MDPI, vol. 5(3), pages 1-16, July.
    17. Eibich, Peter & Ziebarth, Nicolas, 2014. "Examining the Structure of Spatial Health Effects in Germany Using Hierarchical Bayes Models," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 49, pages 305-320.
    18. Wu, Ji & Guo, Mengmeng & Chen, Minghua & Jeon, Bang Nam, 2019. "Market power and risk-taking of banks: Some semiparametric evidence from emerging economies," Emerging Markets Review, Elsevier, vol. 41(C).
    19. repec:jss:jstsof:21:i08 is not listed on IDEAS
    20. Deng, Yaguo, 2016. "Efficiency evaluation of Spanish hotel chains," DES - Working Papers. Statistics and Econometrics. WS 23897, Universidad Carlos III de Madrid. Departamento de Estadística.
    21. Filipe Costa Souza & Wilton Bernardino & Silvio C. Patricio, 2024. "How life-table right-censoring affected the Brazilian social security factor: an application of the gamma-Gompertz-Makeham model," Journal of Population Research, Springer, vol. 41(3), pages 1-38, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:145:y:2016:i:c:p:183-189. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.