Spades and mixture models
F. Bunea, A.B. Tsybakov and M.H. Wegkamp
Annals of Statistics 2009.

Abstract

This paper studies sparse density estimation via L1 penalization (SPADES). We focus on estimation in high-dimensional mixture models and nonparametric adaptive density estimation. We show, respectively, that SPADES can recover, with high probability, the unknown components of a mixture of probability densities and that it yields minimax adaptive density estimates. These results are based on a general sparsity oracle inequality that the SPADES estimates satisfy.

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