Inferences on the Generalized Inverted Exponential Distribution in Constant Stress Partially Accelerated Life Tests Using Generally Progressively Type-II Censored Samples

Abstract

This article discusses different methods for estimating the shape and scale parameters of the generalized inverted exponential distribution (GIED) and the acceleration factor in constant stress partially accelerated life test (CSPALT) with general progressively Type-II censored samples. We obtain the maximum likelihood estimates for the three parameters and calculate correlated approximate confidence intervals. Bayesian point estimates and credible intervals are also determined using the importance sampling method. Monte-Carlo simulation studies are conducted to demonstrate and compare the effectiveness of the proposed parameter estimation techniques. Additionally, a real-life dataset is examined to highlight the practical utility of these methodologies. Our findings indicate that the GIED provides an appropriate and flexible model for the real lifetime data, and the Bayesian approach offers better estimation than classical methods under most scenarios, in terms of using generally progressively Type-II censored samples under CSPALT.

Keywords

• general progressive Type-II censoring
• general inverted exponential distribution
• constant stress partially accelerated life test
• maximum likelihood estimation
• Bayesian estimation
• importance sampling