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Abstract
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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
Submitted: October 15, 2011
Accepted: November 25, 2011
Published: July 15, 2012 |
Abstract:
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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.
Key words: Bayesian analysis, expectation-maximizationtest, markov chain monte carlo simulation, mixture distributions, modified likelihood ratio test, ,
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Abstract
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Cite this Reference:
Rahman Farnoosh, Morteza Ebrahimi and Arezoo Hajirajabi, . Optimal Penalty Functions Based on MCMC for Testing Homogeneity of Mixture Models. Research Journal of Applied Sciences, Engineering and Technology, (14): 2024-2029.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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