We consider simulation-based methods for the design of multi-stress factor accelerated life tests (ALTs) in a Bayesian decision theoretic framework. Multi-stress factor ALTs are challenging due to the increased number of simulation runs required as a result of stress factor-level combinations. We propose the use of Latin hypercube sampling to reduce the simulation cost without loss of statistical efficiency. Exploration and optimization of expected utility function is carried out by a developed algorithm that utilizes Markov chain Monte Carlo methods and nonparametric smoothing techniques. A comparison of proposed approach to a full grid simulation is provided to illustrate computational cost reduction.

Original languageEnglish (US)
Pages (from-to)0
Number of pages1
JournalCommunications in Statistics: Simulation and Computation
StateAccepted/In press - Oct 26 2016



  • Bayesian inference
  • Gibbs sampler
  • Latin hypercube sampling
  • Monte Carlo simulation
  • Nonparametric smoothing

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation

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