Nonparametric Bayesian Analysis of Hazard Rate Functions using the Gamma Process Prior

Richard Arnold, Stefanka Chukova, Yu Hayakawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

When failure time data are modelled using an inhomogeneous Poisson process, it is necessary to model the underlying hazard rate function λ(t). The most common approaches to the problem either select some parametric form for λ(t), or alternatively-conditional on some collected data set- A pproximate it using the non-parametric Kaplan-Meier estimator. In this paper we present simulation and inference for a non-parametric hazard rate function drawn from a Gamma Process Prior. We use a gamma-scaled Dirichlet Process prior to implement the Gamma Process prior, and construct a Markov Chain Monte Carlo sampler to carry out inference. We demon-strate the methodology with the simulation of a process with an increasing failure rate.

Original languageEnglish
Title of host publication2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling, APARM 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728171029
DOIs
Publication statusPublished - 2020 Aug
Event2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling, APARM 2020 - Vancouver, Canada
Duration: 2020 Aug 202020 Aug 23

Publication series

Name2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling, APARM 2020

Conference

Conference2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling, APARM 2020
CountryCanada
CityVancouver
Period20/8/2020/8/23

Keywords

  • Bayesian non-parametrics
  • Gamma Process
  • Hazard rate function.
  • Reliability

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Mechanical Engineering
  • Safety, Risk, Reliability and Quality
  • Modelling and Simulation

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