Abstract
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 language | English (US) |
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Pages (from-to) | 71-90 |
Number of pages | 20 |
Journal | Quality and Reliability Engineering International |
Volume | 40 |
Issue number | 1 |
DOIs | |
State | Published - Feb 2024 |
Keywords
- 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