Statistical Inference for a Simple Step Stress Model with Competing Risks Based on Generalized Type-I Hybrid Censoring

Abstract This paper investigates a simple step-stress accelerated lifetime test (SSALT) model for the inferential analysis of exponential competing risks data. A generalized type-I hybrid censoring scheme is employed to improve efficiency and controllability test. Firstly, MLEs parameters are established based on cumulative exposure (CEM). Then conditional moment generating function (MGF) unknown set up using expectation multiple integral techniques. Thirdly, confidence intervals (CIs) constructed by exact MGF-based method, approximate normality-based bias-corrected (BCa) percentile bootstrap method. Finally, we present simulation studies an illustrative example compare performances different methods.