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 TTIGL . A suggested form of the EM algorithm has been applied on type-II progressive censored samples to obtain the maximum likelihood estimates MLE ′ s of the parameters, survival function SF , and hazard rate function HRF of the studied mixture model. Monte Carlo simulation algorithm has been applied to study the behavior of the mean squares errors MSE ′ s of the estimates. Also, a comparative study is conducted between the suggested 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.