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

Shota Katayama and Shinpei Imori

Journal of Multivariate Analysis, 2014, vol. 132, issue C, 138-150

Abstract: 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.

Keywords: Multivariate linear regression; Model selection; High-dimensional data; Consistency (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.jmva.2014.08.002

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