Penalized Maximum Likelihood Estimation for univariate normal mixture distributions

Due to singularities of the likelihood function, the maximum likelihood approach for the estimation of the parameters of normal mixture models is an acknowledged ill posed optimization problem. Ill posedness is solved by penalizing the likelihood function. In the Bayesian framework, it amounts to incorporating an inverted gamma prior in the likelihood function. A penalized version of the EM algorithm is derived, which is still explicit and which intrinsically assures that the estimates are not singular. Numerical evidence of the latter property is put forward with a test.


Published in:
AIP Conference Proceedings. Bayesian Inference and Maximum Entropy Methods in Science and Engineering: 20th International Workshop., 568, 229-237
Presented at:
Bayesian Inference and Maximum Entropy Methods in Science and Engineering: 20th International Workshop., Gif-sur-Yvette, France, 8-13 July, 2000
Year:
2001
Keywords:
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 Record created 2005-09-16, last modified 2018-03-17

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