Stochastic Approach to EM algorithm for Estimating Exponential-Poisson distribution Parameters Under Progressive Interval Censoring

This paper deals with the problem of estimating unknown parameters of the Exponential-Poisson (EP) distribution as a lifetime modelwhen samples are observed under progressive type-I interval censoring. In the classical expectation maximization (EM) algorithmthe expressions do not admit a closed form. To avoid this situation we next proposed stochastic expectation maximization algorithm(SEM). The performance of the proposed SEM estimators are illustrated by a Monte Carlo simulation study. Our simulation showedthat the performance of SEM algorithm is quite satisfactory on the basis of mean square error and by increasing the sample size, theefficiency is also increases.

Keywords:

Maximum likelihood estimation , Progressive Interval Censoring , EM and SEM algorithm , Exponential Poisson