Estimating a Finite Mixed Exponential Distribution under Progressively Type-II Censored Data

Abstract

The Type-II progressive censoring scheme has become very popular for analyzing lifetime data in reliability and survival analysis. However, no published papers address parameter estimation under progressive Type-II censoring for the mixed exponential distribution (MED), which is an important model for reliability and survival analysis. This is the problem that we address in this paper. It is noted that maximum likelihood estimation of unknown parameters cannot be obtained in closed form due to the complicated log-likelihood function. We solve this problem by using the EM algorithm. Finally, we obtain closed form estimates of the model. The proposed methods are illustrated by both some simulations and a case analysis.

Keywords:

• Mixed exponential distribution
• Progressive Type-II censoring
• EM algorithm
• Maximum likelihood estimation
• Life data analysis.

Mathematics Subject Classification:

• 62N05.