Publications Details
Robustness of Asymptotic Optimal Accelerated Test Plans to Small-Sample Settings
Accelerated testing is a form of testing commonly employed in industry, especially at Sandia National Laboratories, in order to extract information about product lifetime behavior that cannot be acquired under normal operating conditions due to the extensive durations of the intended life- times. As of the writing of this report, there is no known guideline for the design of accelerated tests at the laboratories and yet there is an entire field in the statistical literature on the optimal design of accelerated tests covering a wide range of limitations and constraints available for reference. A potential roadblock to incorporating this knowledge is the common difficulty at Sandia of having severely limited data available for such testing. Nearly all of the methodology presented in the statistical literature is based on asymptotic or large-sample theory and so may not be appropriate for the testing conditions present at Sandia. This report investigates optimal accelerated test plans derived based on small-sample or exact methodology for both a basic test setting as well as a more realistic setting and compares the resulting test plans to those derived based on the large- sample methodology from the literature. It is shown that the large-sample test plans are actually quite robust to the presence of limited data and are generally more flexible than the small-sample test plans.