IDEAS home Printed from https://ideas.repec.org/a/bpj/ecqcon/v30y2015i1p1-8n1.html
   My bibliography  Save this article

Failure Rate Estimation in a Dynamic Environment

Author

Listed:
  • Gouno Evans

    (Université de Bretagne Sud, LMBA-UMR 6205-CNRS/UBO/UBS, Rue Yves Mainguy, BP 573, 56017 Vannes cedex, France)

  • Guérineau Lise

    (EDF R&D Clamart, MIRE, 1 avenue du Général de Gaulle, 92141 Clamart cedex, France)

Abstract

We present a method to assess the reliability of a material operating in a dynamic environment. The dynamic environment is represented as a sequence of shocks governed by a self-exciting point process. The time-to-failure of the material is assumed to have a piecewise exponential distribution. A Cox model is integrated to take into account the effect of the stress. Maximum likelihood estimates of the model parameters are obtained and their properties are studied through simulated data. An application on field data is displayed. Hypothesis testing procedures for environment effect are suggested.

Suggested Citation

  • Gouno Evans & Guérineau Lise, 2015. "Failure Rate Estimation in a Dynamic Environment," Stochastics and Quality Control, De Gruyter, vol. 30(1), pages 1-8, June.
    • Handle: RePEc:bpj:ecqcon:v:30:y:2015:i:1:p:1-8:n:1
    DOI: 10.1515/eqc-2015-6001
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/eqc-2015-6001
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/eqc-2015-6001?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. Dani Gamerman, 1991. "Dynamic Bayesian Models for Survival Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 40(1), pages 63-79, March.
    Full references (including those not matched with items on IDEAS)

    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. K J Wilson & M Farrow, 2010. "Bayes linear kinematics in the analysis of failure rates and failure time distributions," Journal of Risk and Reliability, , vol. 224(4), pages 309-321, December.
    2. Cizek, P. & Lei, J. & Ligthart, J.E., 2012. "The Determinants of VAT Introduction : A Spatial Duration Analysis," Other publications TiSEM 835efbcb-4537-4dab-aaa3-c, Tilburg University, School of Economics and Management.
    3. Ian W. McKeague & Mourad Tighiouart, 2000. "Bayesian Estimators for Conditional Hazard Functions," Biometrics, The International Biometric Society, vol. 56(4), pages 1007-1015, December.
    4. Parfait Munezero, 2022. "Efficient particle smoothing for Bayesian inference in dynamic survival models," Computational Statistics, Springer, vol. 37(2), pages 975-994, April.
    5. Jonathan L. French & Joseph G. Ibrahim, 2002. "Bayesian Methods for a Three–State Model for Rodent Carcinogenicity Studies," Biometrics, The International Biometric Society, vol. 58(4), pages 906-916, December.
    6. Hideo Kozumi, 2000. "Bayesian Analysis of Discrete Survival Data with a Hidden Markov Chain," Biometrics, The International Biometric Society, vol. 56(4), pages 1002-1006, December.
    7. Zuqiang Qiou & Nalini Ravishanker & Dipak K. Dey, 1999. "Multivariate Survival Analysis with Positive Stable Frailties," Biometrics, The International Biometric Society, vol. 55(2), pages 637-644, June.
    8. Luca La Rocca, 2008. "Bayesian Non‐Parametric Estimation of Smooth Hazard Rates for Seismic Hazard Assessment," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(3), pages 524-539, September.
    9. Peter Congdon, 2009. "Life Expectancies for Small Areas: A Bayesian Random Effects Methodology," International Statistical Review, International Statistical Institute, vol. 77(2), pages 222-240, August.
    10. David B. Dunson & Patricia Chulada & Samuel J. Arbes Jr, 2003. "Bayesian Modeling of Time-Varying and Waning Exposure Effects," Biometrics, The International Biometric Society, vol. 59(1), pages 83-91, March.
    11. Madhuja Mallick & Nalini Ravishanker, 2006. "Additive Positive Stable Frailty Models," Methodology and Computing in Applied Probability, Springer, vol. 8(4), pages 541-558, December.
    12. Pavel Čížek & Jinghua Lei & Jenny E. Ligthart, 2017. "Do Neighbours Influence Value-Added-Tax Introduction? A Spatial Duration Analysis," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(1), pages 25-54, February.
    13. Gebrenegus Ghilagaber, 2018. "Environmental recidivism in Sweden: distributional shape and effects of sanctions on duration of compliance," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(2), pages 869-882, March.
    14. Dani Gamerman & Thiago Rezende Santos & Glaura C. Franco, 2013. "A Non-Gaussian Family Of State-Space Models With Exact Marginal Likelihood," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(6), pages 625-645, November.
    15. Bhattacharjee, Arnab & Bhattacharjee, Madhuchhanda, 2007. "Bayesian Analysis of Hazard Regression Models under Order Restrictions on Covariate Effects and Ageing," MPRA Paper 3938, University Library of Munich, Germany.
    16. Godolphin, E.J. & Triantafyllopoulos, Kostas, 2006. "Decomposition of time series models in state-space form," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2232-2246, May.
    17. Michael L. Pennell & David B. Dunson, 2006. "Bayesian Semiparametric Dynamic Frailty Models for Multiple Event Time Data," Biometrics, The International Biometric Society, vol. 62(4), pages 1044-1052, December.
    18. Kostas Triantafyllopoulos, 2009. "Inference of Dynamic Generalized Linear Models: On‐Line Computation and Appraisal," International Statistical Review, International Statistical Institute, vol. 77(3), pages 430-450, December.
    19. T. R. Santos, 2018. "A Bayesian GED-Gamma stochastic volatility model for return data: a marginal likelihood approach," Papers 1809.01489, arXiv.org.

    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:bpj:ecqcon:v:30:y:2015:i:1:p:1-8:n:1. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.