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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

Research 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.
 

Research Date
Research Department
Research Journal
Mathematical Problems in Engineering
Research Member
Research Publisher
Hindawi
Research Rank
1
Research Vol
2022
Research Website
https://doi.org/10.1155/2022/1720033
Research Year
2022
Research Pages
8