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

DOI10.1051/ps/2015011zbMath1392.62179arXiv1410.4682OpenAlexW2286729967MaRDI QIDQ2786498

Emilie Devijver

Publication date: 12 February 2016

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

Full work available at URL: https://arxiv.org/abs/1410.4682

zbMATH Keywords

lasso\(\ell_{1}\)-oracle inequalityfinite mixture of multivariate regression model

Mathematics Subject Classification ID

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

Related Items (2)

Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models ⋮ A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models

Cites Work

• Exponential screening and optimal rates of sparse estimation
• \(\ell_{1}\)-penalization for mixture regression models
• Concentration inequalities and model selection. Ecole d'Eté de Probabilités de Saint-Flour XXXIII -- 2003.
• Estimation of dependences based on empirical data. Transl. from the Russian by Samuel Kotz
• Least angle regression. (With discussion)
• Weak convergence and empirical processes. With applications to statistics
• On the conditions used to prove oracle results for the Lasso
• The Lasso as an \(\ell _{1}\)-ball model selection procedure
• The adaptive and the thresholded Lasso for potentially misspecified models (and a lower bound for the Lasso)
• Simultaneous analysis of Lasso and Dantzig selector
• Anℓ₁-oracle inequality for the Lasso in finite mixture Gaussian regression models
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