A Stochastic EM Algorithm for Progressively Censored Data Analysis

Abstract

Progressive censoring technique is useful in lifetime data analysis. Simple approaches to progressive data analysis are crucial for its widespread adoption by reliability engineers. This study develops an efficient yet easy-to-implement framework for analyzing progressively censored data by making use of the stochastic EM algorithm. Based on this framework, we develop specific stochastic EM procedures for several popular lifetime models. These procedures are shown to be very simple. We then demonstrate the applicability and efficiency of the stochastic EM algorithm by a fatigue life dataset with proper modification and by a progressively censored dataset from a life test on hard disk drives.