Adaptive density estimation for clustering with Gaussian mixtures
  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 {\beta}-H\"older with moment and tail conditions are considered. We show that this penalized estimator is minimax adaptive to the {\beta} regularity of such densities in the Hellinger sense. 
Maugis Cathy (IMT) and Michel Bertrand (LSTA)
math.ST stat.TH