PROJECT TITLE :

Likelihood Inference Under Proportional Hazards Model for One-Shot Device Testing

ABSTRACT:

For devices with long lifetimes, accelerated life-tests are commonly used to induce quick failures. A link function relating stress levels and lifelong is then applied to extrapolate the lifetimes of units from accelerated conditions to traditional operating conditions. As a result of data from one-shot devices do not contain any lifetimes, a commonplace reliability analysis with a parametric distributional assumption on lifetimes may be sensitive to violations of the model assumption. For this reason, we tend to have proposed here a proportional hazards model for analyzing one-shot device testing information collected from constant-stress accelerated life-tests. The utmost probability estimates of the parameters of this semi-parametric model are developed. Confidence intervals for the reliability at an inspection time are constructed through asymptotic and transformation approaches. A Monte Carlo simulation study is then disbursed to match these confidence intervals in terms of coverage possibilities, and average widths. The obtained results show that the proposed versatile semi-parametric model provides a smart insight into the estimation of reliability underneath traditional (typical) operating conditions. A distance-based mostly take a look at statistic is also proposed for testing the proportional hazards model, and the precise calculation of its p-worth is mentioned. Finally, the proposed proportional hazards model is illustrated with real knowledge from a toxicological study.