Bayesian Life Test Planning for the Log-Location-Scale Family of Distributions
This paper describes Bayesian methods for life test planning with censored data from a log-location-scale distribution, when prior information of the distribution parameters is available. We use a Bayesian criterion based on the estimation precision of a distribution quantile. A large sample normal approximation gives a simplified, easy-tointerpret, yet valid approach to this planning problem, where in general no closed form solutions are available. To illustrate this approach, we present numerical investigations using the Weibull distribution with Type II censoring. We also assess the effects of prior distribution choice. A simulation approach of the same Bayesian problem is also presented as a tool for visualization and validation. The validation results generally are consistent with those from the large sample approximation approach.
This preprint was published as Yli Hong, Caleb King, Yao Zhang, and William Q. Meeker, "Bayesian Life Test Planning for the Log-Location-Scale Family of Distributions".