|
Bayesian Estimation of Penalty Function in Homogeneity Test of Mixture Models
|
| Rahman Farnoosh *, Afshin Fallah, Arezoo Hajrajabi |
|
|
|
Abstract: (24157 Views) |
| The modified likelihood ratio test, which is based on penalized likelihood function, is usually
used for testing homogeneity of the mixture models.
The efficiency of this test is seriously affected by the shape of penalty function that is used
in penalized likelihood function.
The selection of penalty function is usually based on avoiding of complexity and increasing tractability,
hence the results may be far from optimality.
In this paper, we consider a more general form of penalty function that depends on a shape parameter. Then this shape
parameter and the parameters of mixture models are estimated by using Bayesian paradigm.
It is shown that the proposed Bayesian approach is more efficient in comparison to modified likelihood test. The
proposed Bayesian approach is clearly more efficient, specially in nonidentifiability situation, where
frequentist approaches are almost failed. |
|
| Keywords: Likelihood Ratio Test, Penalty Function, EM Algorithm, Monte Carlo Markov Chain. Hazard. |
|
|
Full-Text [PDF 556 kb]
(2839 Downloads)
|
Type of Study: Research |
Subject:
Statistical Inference
Received: 2011/07/4 | Accepted: 2013/08/13 | Published: 2020/02/18
|
|
|
|
|
|
|
| Add your comments about this article |
|
|
|
