Fitting discrete phase-type distribution from censored and truncated observations with pre-specified hazard sequence

Abstract

Phase-type distribution allows approximation of non-Markovian models, which permits to analyze complex systems under Markovian deterioration. In addition, reliability data is often composed of truncated and censored observations. This paper presents a novel approach that fits a restricted class of discrete phase-type distribution through pre-specified hazard sequence from incomplete observations. Numerical results are shown using Balakrishnan's mimicked power transformers dataset. Furthermore, it can be used to fit transition probabilities of maintenance optimization's Markov decision process models from incomplete reliability data.