A Proximal Point Algorithm for Minimum Divergence Estimators with Application to Mixture Models†
Laboratoire de Statistique Théorique et Appliquée, Université Pierre et Marie CURIE, 4 place Jussieu, 75005 Paris, France
This paper is an extended version of our paper published in the 2nd Conference on Geometric Science of Information, Palaiseau, France, 28–30 October 2015.
Author to whom correspondence should be addressed.
Academic Editors: Frédéric Barbaresco and Frank Nielsen
Received: 11 June 2016 / Revised: 20 July 2016 / Accepted: 21 July 2016 / Published: 27 July 2016
Estimators derived from a divergence criterion such as
divergences are generally more robust than the maximum likelihood ones. We are interested in particular in the so-called minimum dual
–divergence estimator (MD
DE), an estimator built using a dual representation of
–divergences. We present in this paper an iterative proximal point algorithm that permits the calculation of such an estimator. The algorithm contains by construction the well-known Expectation Maximization (EM) algorithm. Our work is based on the paper of Tseng on the likelihood function. We provide some convergence properties by adapting the ideas of Tseng. We improve Tseng’s results by relaxing the identifiability condition on the proximal term, a condition which is not verified for most mixture models and is hard to be verified for “non mixture” ones. Convergence of the EM algorithm in a two-component Gaussian mixture is discussed in the spirit of our approach. Several experimental results on mixture models are provided to confirm the validity of the approach.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
Share & Cite This Article
MDPI and ACS Style
Al Mohamad, D.; Broniatowski, M. A Proximal Point Algorithm for Minimum Divergence Estimators with Application to Mixture Models. Entropy 2016, 18, 277.
Al Mohamad D, Broniatowski M. A Proximal Point Algorithm for Minimum Divergence Estimators with Application to Mixture Models. Entropy. 2016; 18(8):277.
Al Mohamad, Diaa; Broniatowski, Michel. 2016. "A Proximal Point Algorithm for Minimum Divergence Estimators with Application to Mixture Models." Entropy 18, no. 8: 277.
Show more citation formats
Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
[Return to top]
For more information on the journal statistics, click here
Multiple requests from the same IP address are counted as one view.