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    Abstract
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
Abstract:

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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