Divergence-Based Robust Inference Under Proportional Hazards Model for One-Shot Device Life-Test
N. Balakrishnan, Elena Castilla, Nirian Martín, Leandro Pardo
Abstract
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 Monte–Carlo 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.
Topics & Concepts
EstimatorDivergence (linguistics)Monte Carlo methodInferenceStatisticsComputer scienceApplied mathematicsRobustness (evolution)Maximum likelihoodMathematicsArtificial intelligenceLinguisticsChemistryPhilosophyGeneBiochemistryStatistical Distribution Estimation and ApplicationsOptimal Experimental Design MethodsAdvanced Statistical Process Monitoring