IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v228y2014i2p166-175.html
   My bibliography  Save this article

Bayesian nonparametric models for combining heterogeneous reliability data

Author

Listed:
  • Richard L Warr
  • David H Collins

Abstract

Modern complex engineering systems often present the analyst with a mix of data types that can be used for reliability prediction: system test results, lifetime data from unit tests of components, and subsystem data, all of which may have predictive value for the system lifetime. We present a hierarchical nonparametric framework, using Dirichlet processes, in which time-to-event distributions may be estimated from sample data or derived based on physical failure mechanisms. By applying a Bayesian methodology, the framework can incorporate prior information, including expert opinion.

Suggested Citation

  • 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.
    • Handle: RePEc:sae:risrel:v:228:y:2014:i:2:p:166-175
    DOI: 10.1177/1748006X13503319
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X13503319
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1748006X13503319?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
    ---><---

    References listed on IDEAS

    as
    1. Blum, J. & Susarla, V., 1977. "On the posterior distribution of a dirichlet process given randomly right censored observations," Stochastic Processes and their Applications, Elsevier, vol. 5(3), pages 207-211, July.
    2. Stephen G. Walker & Paul Damien & PuruShottam W. Laud & Adrian F. M. Smith, 1999. "Bayesian Nonparametric Inference for Random Distributions and Related Functions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 485-527.
    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. 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. Antonio Lijoi & Igor Pruenster, 2009. "Models beyond the Dirichlet process," ICER Working Papers - Applied Mathematics Series 23-2009, ICER - International Centre for Economic Research.
    2. Antonio Lijoi & Igor Prunster, 2009. "Models beyond the Dirichlet process," Quaderni di Dipartimento 103, University of Pavia, Department of Economics and Quantitative Methods.
    3. Victor Salinas & José Romeo & Alexis Peña, 2010. "On Bayesian estimation of a survival curve: comparative study and examples," Computational Statistics, Springer, vol. 25(3), pages 375-389, September.
    4. Ryo Kato & Takahiro Hoshino, 2020. "Semiparametric Bayesian multiple imputation for regression models with missing mixed continuous–discrete covariates," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(3), pages 803-825, June.
    5. Christopher A. Bush & Juhee Lee & Steven N. MacEachern, 2010. "Minimally informative prior distributions for non‐parametric Bayesian analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(2), pages 253-268, March.
    6. Xiang Zhang & Yanbing Zheng, 2014. "Nonparametric Bayesian inference for multivariate density functions using Feller priors," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(2), pages 321-340, June.
    7. George Karabatsos & Stephen Walker, 2009. "A Bayesian Nonparametric Approach to Test Equating," Psychometrika, Springer;The Psychometric Society, vol. 74(2), pages 211-232, June.
    8. J. Griffin, 2011. "Bayesian clustering of distributions in stochastic frontier analysis," Journal of Productivity Analysis, Springer, vol. 36(3), pages 275-283, December.
    9. John J. McCall, 2004. "Induction: From Kolmogorov and Solomonoff to De Finetti and Back to Kolmogorov," Metroeconomica, Wiley Blackwell, vol. 55(2‐3), pages 195-218, May.
    10. Dean A. Follmann & Paul S. Albert, 1999. "Bayesian Monitoring of Event Rates with Censored Data," Biometrics, The International Biometric Society, vol. 55(2), pages 603-607, June.
    11. Fellingham, Gilbert W. & Kottas, Athanasios & Hartman, Brian M., 2015. "Bayesian nonparametric predictive modeling of group health claims," Insurance: Mathematics and Economics, Elsevier, vol. 60(C), pages 1-10.
    12. Griffin, J. E. & Steel, M. F. J., 2004. "Semiparametric Bayesian inference for stochastic frontier models," Journal of Econometrics, Elsevier, vol. 123(1), pages 121-152, November.
    13. David B. Dunson & M. Watson & Jack A. Taylor, 2003. "Bayesian Latent Variable Models for Median Regression on Multiple Outcomes," Biometrics, The International Biometric Society, vol. 59(2), pages 296-304, June.
    14. Gary L. Rosner, 2005. "Bayesian Monitoring of Clinical Trials with Failure-Time Endpoints," Biometrics, The International Biometric Society, vol. 61(1), pages 239-245, March.
    15. Krnjajic, Milovan & Kottas, Athanasios & Draper, David, 2008. "Parametric and nonparametric Bayesian model specification: A case study involving models for count data," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2110-2128, January.
    16. Griffin, J.E. & Steel, M.F.J., 2011. "Stick-breaking autoregressive processes," Journal of Econometrics, Elsevier, vol. 162(2), pages 383-396, June.
    17. Luis G. León-Novelo & Peter Müller & Wadih Arap & Mikhail Kolonin & Jessica Sun & Renata Pasqualini & Kim-Anh Do, 2013. "Semiparametric Bayesian Inference for Phage Display Data," Biometrics, The International Biometric Society, vol. 69(1), pages 174-183, March.
    18. repec:jss:jstsof:40:i05 is not listed on IDEAS
    19. Ryo Kato & Takahiro Hoshino, 2018. "Semiparametric Bayes Multiple Imputation for Regression Models with Missing Mixed Continuous-Discrete Covariates," Discussion Paper Series DP2018-15, Research Institute for Economics & Business Administration, Kobe University.
    20. Fernando A. Quintana & Peter Müller & Gary L. Rosner & Mary V. Relling, 2008. "A semiparametric Bayesian model for repeatedly repeated binary outcomes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(4), pages 419-431, September.
    21. Nehla, Debbabi & Marie, Kratz & Mamadou , Mboup, 2016. "A self-calibrating method for heavy tailed data modeling : Application in neuroscience and finance," ESSEC Working Papers WP1619, ESSEC Research Center, ESSEC Business School.

    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:sae:risrel:v:228:y:2014:i:2:p:166-175. 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: SAGE Publications (email available below). General contact details of provider: .

    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.