Data analysis for accelerated life tests with constrained randomization

Kangwon Seo, Rong Pan

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

8 Scopus citations


Accelerated life tests (ALTs) often involve experimental protocols with constrained randomization such as subsampling or random block. As a result, life-time data may construct a grouped structure among the observations. In this paper, we develop a generalized linear mixed model (GLMM) approach for analyzing ALT data with a grouped structure in order to reflect random effects of groups in the model. The GLMM approach provides a flexible way to model censored failure time data with random effects. Particularly, for Weibull failure time distribution, we describe an iterative procedure for the model parameters estimation and derive the asymptotic variance-covariance matrix using the approximated likelihood function. Two examples of life-time data with subsampling and random block are analyzed by the proposed method, which is implemented by modern computer software.

Original languageEnglish (US)
Title of host publicationProceedings - Annual Reliability and Maintainability Symposium
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781509002481
StatePublished - Apr 5 2016
EventAnnual Reliability and Maintainability Symposium, RAMS 2016 - Tucson, United States
Duration: Jan 25 2016Jan 28 2016


OtherAnnual Reliability and Maintainability Symposium, RAMS 2016
Country/TerritoryUnited States


  • Accelerated life test
  • generalized linear mixed model
  • maximum likelihood estimation
  • random effects

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Mathematics(all)
  • Computer Science Applications


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