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 : https://doi.org/10.1051/ps/2012018
Classification : 62G07,  62G20
Mots clés : rate adaptive density estimation, gaussian mixture clustering, hellinger risk, non asymptotic model selection
@article{PS_2013__17__698_0,
author = {Maugis-Rabusseau, C. and Michel, B.},
title = {Adaptive density estimation for clustering with gaussian mixtures},
journal = {ESAIM: Probability and Statistics},
pages = {698--724},
publisher = {EDP-Sciences},
volume = {17},
year = {2013},
doi = {10.1051/ps/2012018},
mrnumber = {3126158},
language = {en},
url = {http://www.numdam.org/articles/10.1051/ps/2012018/}
}
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://www.numdam.org/articles/10.1051/ps/2012018/

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