Semiparametric Bayesian optimal replacement policies: application to railroad tracks

Jason R. Merrick and Refik Soyer

Applied Stochastic Models in Business and Industry, 2017, vol. 33, issue 5, 445-460

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.

Date: 2017
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https://doi.org/10.1002/asmb.2210

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Persistent link: https://EconPapers.repec.org/RePEc:wly:apsmbi:v:33:y:2017:i:5:p:445-460

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