Reliability Inference in Constant Stress Accelerated Life Tests Based on the Exponentiated Half-Logistic Model

Accelerated life testing is an efficient methodology that enables the acquisition of failure time data in a shorter period by subjecting test units to different stress levels, compared to tests conducted under normal operating conditions. In this study, it is assumed that the lifetime of a product under a constant stress level follows an exponentiated half-logistic distribution and the model parameters, reliability function and mean time to failure function are estimated using eleven different frequentist methods, namely, maximum likelihood, least squares, weighted least squares, Cramér–von Mises, Anderson–Darling, right-tail Anderson–Darling, left-tail Anderson–Darling, second-order left-tail Anderson–Darling, maximum product of spacings, minimum spacing absolute distance, and minimum spacing absolute-log distance. The performances of the estimators are compared through an extensive Monte Carlo simulation study. Furthermore, a real dataset is analyzed to evaluate the practical effectiveness of the proposed methods.