Robustness of asymptotic accelerated life test plans to small-sample settings

Much of the statistical literature on optimal test planning for accelerated life testing utilize asymptotic methods to derive optimal test plans. While sufficient effort is made to assess the robustness of these test plans to the choice of design parameters and distribution assumptions, there is very little literature on the performance of asymptotic test plans relative to small samples (on the order of 10-15 samples). An alternative concern is that the asymptotic test plans may not necessarily be the true "optimal" test plan for a given sample size. The purpose of this research is to present exact or "near-exact" methods for developing test plans and compare the performance of these test plans with corresponding asymptotic test plans in small-sample settings. The optimal location of design points and sample allocation is determined using each method for lognormal and Weibull lifetime distributions with both complete and Type 1 right-censored data under two selected acceleration factor models. The investigations reveal that asymptotic test plans tend to corroborate quite well with exact test plans and thus are suitably robust to small-sample settings in terms of optimal variance.