Statistical Inference of Accelerated Life Tests with Masked Interval Data under Tampered Failure Rate Model

In this work, we consider the statistical analysis for step-stress accelerated life test in the presence of competing risks, where a product or an item is exposed to multiple risks and a failure can be caused by one of the multiple risks. Due to cost consideration or environmental restrictions, the cause of failure for an item is not exactly known. Then, masked cause of failure might occur in the collected data. The competing risks distribution is assumed to follow the Weibull distribution with common shape parameter. Interval cencoring is allowed and tampered failure rate model is hold. We apply the maximum likelihood approach via the expectation-maximization algorithm and use the parametric bootstrap method for constructing confidence interval of the unknown parameters. Finally, the precision of the estimates is assessed through Monte Carlo simulations and bootstrap confidence intervals are derived