Optimal Allocation for Extreme Value Regression Under Time Censoring

Abstract

In this paper, we discuss the optimal allocation problem in a multi-level accelerated life testing experiment under time (Type-I) censoring when an extreme-value regression model is used for statistical analysis. We derive the expected Fisher information and the asymptotic variance-covariance matrix of the maximum likelihood estimators. Three optimality criteria are considered and the corresponding optimal allocations are determined. Under Type-I censoring, because the optimal allocations are depending on the model parameters, the sensitivity of the optimal allocations due to mis-specification of the model parameters is studied. A numerical example is used to illustrate the methodologies developed in this paper.