Bayesian D-optimal design for life testing with censoring

Dustin Taylor, Steven E. Rigdon, Rong Pan, Douglas C. Montgomery

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


The assumption of normality is usually tied to the design and analysis of an experimental study. However, when dealing with lifetime testing and censoring at fixed time intervals, we can no longer assume that the outcomes will be normally distributed. This generally requires the use of optimal design techniques to construct the test plan for specific distribution of interest. Optimal designs in this situation depend on the parameters of the distribution, which are generally unknown a priori. A Bayesian approach can be used by placing a prior distribution on the parameters, thereby leading to an appropriate selection of experimental design. This, along with the model and number of predictors, can be used to derive the D-optimal design for an allowed number of experimental runs. This paper explores using this Bayesian approach on various lifetime regression models to select appropriate D-optimal designs in regular and irregular design regions.

Original languageEnglish (US)
Pages (from-to)71-90
Number of pages20
JournalQuality and Reliability Engineering International
Issue number1
StatePublished - Feb 2024


  • Bayesian design
  • coordinate exchange algorithm
  • irregular design region
  • life testing
  • optimal design

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Management Science and Operations Research


Dive into the research topics of 'Bayesian D-optimal design for life testing with censoring'. Together they form a unique fingerprint.

Cite this