| Abstract |
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. |
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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