EM Algorithm for Truncated and Censored Poisson Likelihoods

The aim of this study is to find the maximum likelihood estimate (MLE) among frequency count data by using the expectation-maximization (EM) algorithm in which is useful to impute the missing or hidden values. Two forms of missing count data in both zero truncation and right censoring situations are illustrated for estimating the population size on drug use. The results show that a truncated and censored Poisson likelihood performs well with good estimates corresponding to the EM algorithm with a numerically stable convergence, a monotone increasing likelihood, and providing local maxima, so the expected global maximum of the MLE depends on the initial value.