Optimal sample size allocation for multi-level stress testing with exponential regression under type-I censoring

In this article, we discuss the optimal allocation problem in a multi-level accelerated life-testing experiment under Type-I censoring when an exponential regression model is used for the analysis. We derive the expected Fisher information matrix and use it to obtain the asymptotic variance–covariance matrix of the maximum likelihood estimators (MLEs). We then consider the optimal allocation under the D-optimality criterion, and present an algorithm for determining the optimal allocation. A numerical example is presented for the purpose of illustration. The optimal allocation depends on the model parameters and so a sensitivity analysis of the optimal allocation to misspecification of the model parameters is carried out as well.