Sparsity considerations for dependent observations

Pierre Alquier (1, 2) , Paul Doukhan (3)

1 LPMA - Laboratoire de Probabilités et Modèles Aléatoires
2 CREST - Centre de Recherche en Économie et Statistique
3 AGM - UMR 8088 - Analyse, Géométrie et Modélisation

The aim of this paper is to provide a comprehensive introduction for the study of L1-penalized estimators in the context of dependent observations. We define a general $\ell_{1}$-penalized estimator for solving problems of stochastic optimization. This estimator turns out to be the LASSO in the regression estimation setting. Powerful theoretical guarantees on the statistical performances of the LASSO were provided in recent papers, however, they usually only deal with the iid case. Here, we study our estimator under various dependence assumptions.

Mots clés: density estimation; Estimation in high dimensional; weak dependence; sparsity; penalization; LASSO; regression estimation; density estimation.

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Pierre Alquier, Paul Doukhan. Sparsity considerations for dependent observations. 2011. ⟨hal-00564291v1⟩