Anℓ1-oracle inequality for the Lasso in finite mixture Gaussian regression models

DOI10.1051/PS/2012016zbMath1395.62166OpenAlexW2032936552MaRDI QIDQ5408490

Caroline Meynet

Publication date: 10 April 2014

Published in: ESAIM: Probability and Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1051/ps/2012016

zbMATH Keywords

Lasso\(\ell_1\)-balls\(\ell_1\)-oracle inequalitiesmodel selection by penalizationfinite mixture of Gaussian regressions model

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

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Anℓ₁-oracle inequality for the Lasso in multivariate finite mixture of multivariate Gaussian regression models ⋮ A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models

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