Statistical planning and inference in accelerated life testing using the CHSS model

Abstract

Failures of highly reliable units are rare and it may be not possible to gather the failure time data needed for reliability estimation. One way of obtaining failures during the time given for experiments is to apply methods of accelerated life testing (ALT). In ALT units are tested at higher than usual (design) stress conditions. The purpose is to give estimators of the main reliability characteristics of units functioning under the usual stress using data of accelerated experiments. To treat such data accelerated life models are used. Here we consider special plans of experiments and the statistical analysis of the ALT data by numerical methods and simulation using the changing shape and scale (CHSS) model proposed by Bagdonavičius and Nikulin (1999). The CHSS model is a natural extension of the standard accelerated failure time (AFT) model. We give parametric and semiparametric estimation procedures for the CHSS model and a goodness-of-fit test for the AFT model.