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2012 (Vol. 4, Issue: 14)
Article Information:

Optimal Penalty Functions Based on MCMC for Testing Homogeneity of Mixture Models

Rahman Farnoosh, Morteza Ebrahimi and Arezoo Hajirajabi
Corresponding Author:  Rahman Farnoosh 

Key words:  Bayesian analysis, expectation-maximizationtest, markov chain monte carlo simulation, mixture distributions, modified likelihood ratio test, ,
Vol. 4 , (14): 2024-2029
Submitted Accepted Published
October 15, 2011 November 25, 2011 July 15, 2012

This study is intended to provide an estimation of penalty function for testing homogeneity of mixture models based on Markov chain Monte Carlo simulation. The penalty function is considered as a parametric function and parameter of determinative shape of the penalty function in conjunction with parameters of mixture models are estimated by a Bayesian approach. Different mixture of uniform distribution are used as prior. Some simulation examples are perform to confirm the efficiency of the present work in comparison with the previous approaches.
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  Cite this Reference:
Rahman Farnoosh, Morteza Ebrahimi and Arezoo Hajirajabi, 2012. Optimal Penalty Functions Based on MCMC for Testing Homogeneity of Mixture Models.  Research Journal of Applied Sciences, Engineering and Technology, 4(14): 2024-2029.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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