TY - JOUR
T1 - Lasso penalized model selection criteria for high-dimensional multivariate linear regression analysis
AU - Katayama, Shota
AU - Imori, Shinpei
N1 - Funding Information:
We would like to thank the associate editor and the two referees for their careful reading of the original article and for their valuable comments that greatly helped improve the article. This work was supported by the JSPS Grant-in-Aid for JSPS Fellows Number 23-2789 .
Publisher Copyright:
© 2014 Elsevier Inc.
PY - 2014/11/1
Y1 - 2014/11/1
N2 - This paper proposes two model selection criteria for identifying relevant predictors in the high-dimensional multivariate linear regression analysis. The proposed criteria are based on a Lasso type penalized likelihood function to allow the high-dimensionality. Under the asymptotic framework that the dimension of multiple responses goes to infinity while the maximum size of candidate models has smaller order of the sample size, it is shown that the proposed criteria have the model selection consistency, that is, they can asymptotically pick out the true model. Simulation studies show that the proposed criteria outperform existing criteria when the dimension of multiple responses is large.
AB - This paper proposes two model selection criteria for identifying relevant predictors in the high-dimensional multivariate linear regression analysis. The proposed criteria are based on a Lasso type penalized likelihood function to allow the high-dimensionality. Under the asymptotic framework that the dimension of multiple responses goes to infinity while the maximum size of candidate models has smaller order of the sample size, it is shown that the proposed criteria have the model selection consistency, that is, they can asymptotically pick out the true model. Simulation studies show that the proposed criteria outperform existing criteria when the dimension of multiple responses is large.
KW - Consistency
KW - High-dimensional data
KW - Model selection
KW - Multivariate linear regression
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U2 - 10.1016/j.jmva.2014.08.002
DO - 10.1016/j.jmva.2014.08.002
M3 - Article
AN - SCOPUS:84908554860
SN - 0047-259X
VL - 132
SP - 138
EP - 150
JO - Journal of Multivariate Analysis
JF - Journal of Multivariate Analysis
ER -