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Copula Model Selection for Vehicle Component Failures Based on Warranty Claims

Author

Listed:
  • Kathryn Wifvat

    (School of Mathematics and Statistical Sciences, Arizona State University, Tempe, AZ 85821, USA)

  • John Kumerow

    (Target Corporation, Minneapolis, MN 55405, USA)

  • Arkady Shemyakin

    (College of Arts and Sciences, University of St. Thomas, Saint Paul, MN 55105, USA)

Abstract

In the automotive industry, it is important to know whether the failure of some car parts may be related to the failure of others. This project studies warranty claims for five engine components obtained from a major car manufacturer with the purpose of modeling the joint distributions of the failure of two parts. The one-dimensional distributions of components are combined to construct a bivariate copula model for the joint distribution that makes it possible to estimate the probabilities of two components failing before a given time. Ultimately, the influence of the failure of one part on the operation of another related part can be described, predicted, and addressed. The performance of several families of one-parameter Archimedean copula models (Clayton, Gumbel–Hougaard, survival copulas) is analyzed, and Bayesian model selection is performed. Both right censoring and conditional approaches are considered with the emphasis on conditioning to the warranty period.

Suggested Citation

  • Kathryn Wifvat & John Kumerow & Arkady Shemyakin, 2020. "Copula Model Selection for Vehicle Component Failures Based on Warranty Claims," Risks, MDPI, vol. 8(2), pages 1-15, June.
    • Handle: RePEc:gam:jrisks:v:8:y:2020:i:2:p:56-:d:365720
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    References listed on IDEAS

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    Cited by:

    1. Shokouhyar, Sajjad & Ahmadi, Sadra & Ashrafzadeh, Mahdi, 2021. "Promoting a novel method for warranty claim prediction based on social network data," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Lin, Kunsong & Chen, Yunxia, 2021. "Analysis of two-dimensional warranty data considering global and local dependence of heterogeneous marginals," Reliability Engineering and System Safety, Elsevier, vol. 207(C).

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