Exponential-polynomial divergence based inference for nondestructive one-shot devices under progressive stress model

Nondestructive one-shot device (NOSD) testing plays a crucial role in engineering, particularly in the reliability assessment of high-stakes systems such as aerospace components, medical devices, and semiconductor technologies. Accurate reliability prognosis of NOSD testing data is essential for ensuring product durability, safety, and performance optimization. The conventional estimation methods like maximum likelihood estimation (MLE) are sensitive to data contamination, leading to biased results. Consequently, this study develops robust inferential analysis for NOSD testing data under a progressive stress model. The lifetime of NOSD is assumed to follow Log-logistic distribution. The estimation procedure addresses robustness by incorporating Exponential-polynomial divergence (EPD). Equipped with three tuning parameters, EPD based estimation is proven to be more flexible than density power divergence estimation frequently used for one-shot device testing data analysis. Further, we explore the asymptotic behaviour of minimum EPD estimator (MEPDE) for large sample size. The robustness of MEPDE is analytically studied through influence function. Since tradeoff between efficiency and robustness of EPD based estimation is governed by three tuning parameters, a novel approach leveraging Concrete Score Matching (CSM) is introduced to optimize the tuning parameters of MEPDE. Moreover, a comparative study with the existing methods of finding tuning parameters is conducted through extensive simulation experiment and data analysis. Another aspect of this study is determining an optimal plan to ensure a successful ALT experiment within specified budget and time constraints. It is designed on A-optimality criteria subject to the given constraints and is executed using the constraint particle swarm optimization (CPSO) algorithm.