A Monte Carlo Expectation Maximization Algorithm for Statistical Inference of Weibull Process with Left Censored Data

Abstract

Abstract The Weibull process plays an important role in the failure analysis of repairable systems. In practice, there exists a situation that the data collected are incomplete. Some of the failure data are missing due to various reasons. Statistical inferences of a Weibull process with incomplete data using Monte Carlo expectation maximization algorithm is proposed. The estimation procedures are derived. A case study is performed to illustrate and compare the performances of this algorithm. It is observed that this method is effective and can simplify the estimation.