Bayesian nonparametric modeling of heterogeneous time-to-event data with an unknown number of sub-populations

Mingyang Li, Jiali Han, Jian Liu

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Time-to-event data are a broad class of data widely encountered at different stages of the product life cycle. In practice, time-to-event data often exhibit heterogeneity, due to a variety of design and manufacturing issues, such as material quality inhomogeneity, unverified design changes, and manufacturing defects. Existing time-to-event modeling approaches mainly ignore this heterogeneity or account for it by pre-determining a fixed number of sub-populations. However, neglecting heterogeneity hinders the modeling accuracy, whereas pre-determining the number of sub-populations is often subjective or unjustifiable. In this article, a Bayesian nonparametric model is proposed to model heterogeneous time-to-event data by assuming an unknown number of sub-populations and quantifying the influence of possible covariates. An estimation algorithm is further proposed to achieve the joint model estimation and selection and to deal with the non-conjugate priors. Case studies demonstrate the effectiveness of the proposed work.

Original languageEnglish (US)
Pages (from-to)481-492
Number of pages12
JournalIISE Transactions
Volume49
Issue number5
DOIs
StatePublished - 2017

Keywords

  • Bayesian learning
  • Hazard regression
  • Latent heterogeneity
  • Non-conjugate prior
  • Nonparametric model

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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