Adaptive density estimation for clustering with gaussian mixtures
ESAIM: Probability and Statistics, Tome 17 (2013), pp. 698-724.

Gaussian mixture models are widely used to study clustering problems. These model-based clustering methods require an accurate estimation of the unknown data density by Gaussian mixtures. In Maugis and Michel (2009), a penalized maximum likelihood estimator is proposed for automatically selecting the number of mixture components. In the present paper, a collection of univariate densities whose logarithm is locally β-Hölder with moment and tail conditions are considered. We show that this penalized estimator is minimax adaptive to the β regularity of such densities in the Hellinger sense.

DOI : 10.1051/ps/2012018
Classification : 62G07, 62G20
Mots-clés : rate adaptive density estimation, gaussian mixture clustering, hellinger risk, non asymptotic model selection
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     title = {Adaptive density estimation for clustering with gaussian mixtures},
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     url = {http://archive.numdam.org/articles/10.1051/ps/2012018/}
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Maugis-Rabusseau, C.; Michel, B. Adaptive density estimation for clustering with gaussian mixtures. ESAIM: Probability and Statistics, Tome 17 (2013), pp. 698-724. doi : 10.1051/ps/2012018. http://archive.numdam.org/articles/10.1051/ps/2012018/

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