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Semiparametric Bayesian optimal replacement policies: application to railroad tracks

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  • Jason R. Merrick
  • Refik Soyer

Abstract

We present a Bayesian decision theoretic approach for developing replacement strategies. In so doing, we consider a semiparametric model to describe the failure characteristics of systems by specifying a nonparametric form for cumulative intensity function and by taking into account effect of covariates by a parametric form. Use of a gamma process prior for the cumulative intensity function complicates the Bayesian analysis when the updating is based on failure count data. We develop a Bayesian analysis of the model using Markov chain Monte Carlo methods and determine replacement strategies. Adoption of Markov chain Monte Carlo methods involves a data augmentation algorithm. We show the implementation of our approach using actual data from railroad tracks. Copyright © 2016 John Wiley & Sons, Ltd.

Suggested Citation

  • Jason R. Merrick & Refik Soyer, 2017. "Semiparametric Bayesian optimal replacement policies: application to railroad tracks," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(5), pages 445-460, September.
    • Handle: RePEc:wly:apsmbi:v:33:y:2017:i:5:p:445-460
    DOI: 10.1002/asmb.2210
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    Cited by:

    1. Antonio Pievatolo & Fabrizio Ruggeri & Refik Soyer & Simon Wilson, 2021. "Decisions in Risk and Reliability: An Explanatory Perspective," Stats, MDPI, vol. 4(2), pages 1-23, March.
    2. Insua, David Rios & Ruggeri, Fabrizio & Soyer, Refik & Wilson, Simon, 2020. "Advances in Bayesian decision making in reliability," European Journal of Operational Research, Elsevier, vol. 282(1), pages 1-18.
    3. Kaan Kuzu & Refik Soyer, 2018. "Bayesian modeling of abandonments in ticket queues," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(6-7), pages 499-521, September.

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