Divergence-Based Robust Inference Under Proportional Hazards Model for One-Shot Device Life-Test

In this article, we develop robust estimators and tests for one-shot device testing under proportional hazards assumption based on divergence measures. Through a detailed MonteCarlo simulation study and a numerical example, the developed inferential procedures are shown to be more robust against data contamination than the classical procedures, based on maximum likelihood estimators.