Spades and Mixture Models
نویسندگان
چکیده
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. MSC2000 Subject classification: Primary 62G08, Secondary 62C20, 62G05, 62G20
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