Estimation Using Suggested EM Algorithm Based on Progressively Type-II Censored Samples from a Finite Mixture of Truncated Type-I Generalized Logistic Distributions with an Application

Abstract

In this paper, the identifiability property has been studied for a suggested truncated type-I generalized logistic mixture model which is denoted by $\left(\text{TTIGL}\right)$ . A suggested form of the $\text{EM}$ algorithm has been applied on type-II progressive censored samples to obtain the maximum likelihood estimates $\left(\text{MLE}\prime s\right)$ of the parameters, survival function $\left(\text{SF}\right)$ , and hazard rate function $\left(\text{HRF}\right)$ of the studied mixture model. Monte Carlo simulation algorithm has been applied to study the behavior of the mean squares errors $\left({\text{MSE}}^{\prime }s\right)$ of the estimates. Also, a comparative study is conducted between the suggested $\text{EM}$ algorithm and the ordinary algorithm of maximizing the likelihood function, which depends on the differentiation of the log likelihood function. The results of this paper have been applied on a real dataset as an application.