Lasso penalized model selection criteria for high-dimensional multivariate linear regression analysis

DOI10.1016/J.JMVA.2014.08.002zbMath1360.62382OpenAlexW2074199345MaRDI QIDQ458641

Shinpei Imori, Shota Katayama

Publication date: 8 October 2014

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2014.08.002

zbMATH Keywords

consistencymodel selectionhigh-dimensional datamultivariate linear regression

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

Related Items (3)

Generalized canonical correlation variables improved estimation in high dimensional seemingly unrelated regression models ⋮ Asymptotic Optimality of Cp-Type Criteria in High-Dimensional Multivariate Linear Regression Models ⋮ Consistent variable selection criteria in multivariate linear regression even when dimension exceeds sample size

Uses Software

• glasso

Cites Work

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