Sensitivity analysis of optimal designs for accelerated life testing

In experimental testing, it is desirable to select combinations of input factors that yield optimal results given an objective specified by a practitioner. Often this objective involves minimizing the uncertainty in a parameter or prediction estimate. To determine the experimental design to achieve such an objective, it is necessary to understand the relationship between the testing variables and the response. Alphabetic optimal designs are commonly used in such applications based largely on the ease of their construction with advanced statistical software. However, many publications cite concerns with the overall robustness of such designs to small departures in parameter estimates. This paper examines the issue of model parameter sensitivity to the selection of an accelerated life test based on the UC-optimality criterion, minimizing of the prediction variance at the usage condition, using a generalized linear model framework. We will examine the trade-off implications of choice of experimental design, sample size, and censoring.