Bayesian hazard modeling based on lifetime data with latent heterogeneity

Mingyang Li, Jian Liu

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Lifetime data collected from reliability tests or field operations often exhibit significant heterogeneity patterns caused by latent factors. Such latent heterogeneity indicates that lifetime observations may belong to different sub-populations with different distribution parameters. As a result, the assumption on data homogeneity adopted by conventional reliability modeling techniques becomes inappropriate. Effective identification and quantification of such heterogeneity is crucial for more reliable model estimation and subsequent optimal decision making in a variety of reliability assurance activities. This research proposes a full Bayesian modeling framework for statistical hazard modeling of latent heterogeneity in lifetime data. The proposed framework is generic and comprehensive by systematically addressing different modeling aspects, which include modeling sub-populations with different hazard rates changing over time and different responses to the same stress factors, determining the number of sub-populations, identifying the most appropriate sub-population model structures, estimating model parameters and performing predictive inference. A numerical case study demonstrates the validity and effectiveness of the proposed approach.

Original languageEnglish (US)
Pages (from-to)183-189
Number of pages7
JournalReliability Engineering and System Safety
Volume145
DOIs
StatePublished - Jan 1 2016

Fingerprint

Lifetime Data
Hazard
Hazards
Modeling
Predictive Inference
Model structures
Reliability Modeling
Bayesian Modeling
Hazard Rate
Population Model
Decision making
Homogeneity
Quantification
Lifetime
Decision Making
Model
Demonstrate
Framework

Keywords

  • Bayesian inference
  • Gibbs sampler
  • Hazard regression
  • Mixture model
  • Model selection

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality
  • Applied Mathematics

Cite this

Bayesian hazard modeling based on lifetime data with latent heterogeneity. / Li, Mingyang; Liu, Jian.

In: Reliability Engineering and System Safety, Vol. 145, 01.01.2016, p. 183-189.

Research output: Contribution to journalArticle

@article{c85764920c114bab8eb0aad795a40d75,
title = "Bayesian hazard modeling based on lifetime data with latent heterogeneity",
abstract = "Lifetime data collected from reliability tests or field operations often exhibit significant heterogeneity patterns caused by latent factors. Such latent heterogeneity indicates that lifetime observations may belong to different sub-populations with different distribution parameters. As a result, the assumption on data homogeneity adopted by conventional reliability modeling techniques becomes inappropriate. Effective identification and quantification of such heterogeneity is crucial for more reliable model estimation and subsequent optimal decision making in a variety of reliability assurance activities. This research proposes a full Bayesian modeling framework for statistical hazard modeling of latent heterogeneity in lifetime data. The proposed framework is generic and comprehensive by systematically addressing different modeling aspects, which include modeling sub-populations with different hazard rates changing over time and different responses to the same stress factors, determining the number of sub-populations, identifying the most appropriate sub-population model structures, estimating model parameters and performing predictive inference. A numerical case study demonstrates the validity and effectiveness of the proposed approach.",
keywords = "Bayesian inference, Gibbs sampler, Hazard regression, Mixture model, Model selection",
author = "Mingyang Li and Jian Liu",
year = "2016",
month = "1",
day = "1",
doi = "10.1016/j.ress.2015.09.007",
language = "English (US)",
volume = "145",
pages = "183--189",
journal = "Reliability Engineering and System Safety",
issn = "0951-8320",
publisher = "Elsevier Limited",

}

TY - JOUR

T1 - Bayesian hazard modeling based on lifetime data with latent heterogeneity

AU - Li, Mingyang

AU - Liu, Jian

PY - 2016/1/1

Y1 - 2016/1/1

N2 - Lifetime data collected from reliability tests or field operations often exhibit significant heterogeneity patterns caused by latent factors. Such latent heterogeneity indicates that lifetime observations may belong to different sub-populations with different distribution parameters. As a result, the assumption on data homogeneity adopted by conventional reliability modeling techniques becomes inappropriate. Effective identification and quantification of such heterogeneity is crucial for more reliable model estimation and subsequent optimal decision making in a variety of reliability assurance activities. This research proposes a full Bayesian modeling framework for statistical hazard modeling of latent heterogeneity in lifetime data. The proposed framework is generic and comprehensive by systematically addressing different modeling aspects, which include modeling sub-populations with different hazard rates changing over time and different responses to the same stress factors, determining the number of sub-populations, identifying the most appropriate sub-population model structures, estimating model parameters and performing predictive inference. A numerical case study demonstrates the validity and effectiveness of the proposed approach.

AB - Lifetime data collected from reliability tests or field operations often exhibit significant heterogeneity patterns caused by latent factors. Such latent heterogeneity indicates that lifetime observations may belong to different sub-populations with different distribution parameters. As a result, the assumption on data homogeneity adopted by conventional reliability modeling techniques becomes inappropriate. Effective identification and quantification of such heterogeneity is crucial for more reliable model estimation and subsequent optimal decision making in a variety of reliability assurance activities. This research proposes a full Bayesian modeling framework for statistical hazard modeling of latent heterogeneity in lifetime data. The proposed framework is generic and comprehensive by systematically addressing different modeling aspects, which include modeling sub-populations with different hazard rates changing over time and different responses to the same stress factors, determining the number of sub-populations, identifying the most appropriate sub-population model structures, estimating model parameters and performing predictive inference. A numerical case study demonstrates the validity and effectiveness of the proposed approach.

KW - Bayesian inference

KW - Gibbs sampler

KW - Hazard regression

KW - Mixture model

KW - Model selection

UR - http://www.scopus.com/inward/record.url?scp=84944104591&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84944104591&partnerID=8YFLogxK

U2 - 10.1016/j.ress.2015.09.007

DO - 10.1016/j.ress.2015.09.007

M3 - Article

VL - 145

SP - 183

EP - 189

JO - Reliability Engineering and System Safety

JF - Reliability Engineering and System Safety

SN - 0951-8320

ER -