A fast and consistent variable selection method for high-dimensional multivariate linear regression with a large number of explanatory variables

DOI10.1214/20-EJS1701zbMath1439.62166MaRDI QIDQ2180065

Hirokazu Yanagihara, Ryoya Oda

Publication date: 13 May 2020

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ejs/1585360818

zbMATH Keywords

consistencyvariable selectionmultivariate linear regressionhigh-dimensional asymptotic framework

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Asymptotic distribution theory in statistics (62E20) Linear regression; mixed models (62J05)

Related Items (4)

An \(\ell_{2,0}\)-norm constrained matrix optimization via extended discrete first-order algorithms ⋮ Strong consistency of log-likelihood-based information criterion in high-dimensional canonical correlation analysis ⋮ High-dimensional consistencies of KOO methods in multivariate regression model and discriminant analysis ⋮ Improved estimators for semi-supervised high-dimensional regression model

Uses Software

• glmnet

Cites Work

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