Guo, Wenxing and Balakrishnan, Narayanaswamy and He, Mu (2023) Envelope-based sparse reduced-rank regression for multivariate linear model. Journal of Multivariate Analysis, 195. p. 105159. DOI https://doi.org/10.1016/j.jmva.2023.105159 (In Press)
Guo, Wenxing and Balakrishnan, Narayanaswamy and He, Mu (2023) Envelope-based sparse reduced-rank regression for multivariate linear model. Journal of Multivariate Analysis, 195. p. 105159. DOI https://doi.org/10.1016/j.jmva.2023.105159 (In Press)
Guo, Wenxing and Balakrishnan, Narayanaswamy and He, Mu (2023) Envelope-based sparse reduced-rank regression for multivariate linear model. Journal of Multivariate Analysis, 195. p. 105159. DOI https://doi.org/10.1016/j.jmva.2023.105159 (In Press)
Abstract
Envelope models were first proposed by Cook et al. (2010) as a method to reduce estimative and predictive variations in multivariate regression. Sparse reduced-rank regression, introduced by Chen and Huang (2012), is a widely used technique that performs dimension reduction and variable selection simultaneously in multivariate regression. In this work, we combine envelope models and sparse reduced-rank regression method to propose an envelope-based sparse reduced-rank regression estimator, and then establish its consistency, asymptotic normality and oracle property in high-dimensional data. We carry out some Monte Carlo simulation studies and also analyze two datasets to demonstrate that the proposed envelope-based sparse reduced-rank regression method displays good variable selection and prediction performance.
Item Type: | Article |
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Uncontrolled Keywords: | Dimension reduction; Envelope model; High dimension; Reduced-rank regression; Variable selection |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Mathematics, Statistics and Actuarial Science, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 03 Feb 2023 16:57 |
Last Modified: | 16 May 2024 21:40 |
URI: | http://repository.essex.ac.uk/id/eprint/34553 |
Available files
Filename: Envelope-based sparse reduced-rank regression.pdf
Licence: Creative Commons: Attribution-Noncommercial-No Derivative Works 4.0