A note on rank reduction in sparse multivariate regression

DOI10.1080/15598608.2015.1081573zbMath1420.62284OpenAlexW1614481068WikidataQ36702333 ScholiaQ36702333MaRDI QIDQ2323156

Kun Chen, Kung-Sik Chan

Publication date: 30 August 2019

Published in: Journal of Statistical Theory and Practice (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc4797956

zbMATH Keywords

singular value decompositionsparsityreduced-rankvector autoregressive modelrank selection

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) General nonlinear regression (62J02)

Related Items (3)

Unnamed Item ⋮ Spectra in low‐rank localized layers (SpeLLL) for interpretable time–frequency analysis ⋮ Parallel integrative learning for large-scale multi-response regression with incomplete outcomes

Uses Software

• R
• PMA

Cites Work

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• A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis
• Reduced rank regression via adaptive nuclear norm penalization
• The Adaptive Lasso and Its Oracle Properties
• Optimal selection of reduced rank estimators of high-dimensional matrices
• Subset ARMA selection via the adaptive Lasso
• Joint variable and rank selection for parsimonious estimation of high-dimensional matrices
• Reduced rank vector generalized linear models for feature extraction
• On the distribution of the left singular vectors of a random matrix and its applications
• Regularized multivariate regression for identifying master predictors with application to integrative genomics study of breast cancer
• Reduced-rank regression for the multivariate linear model
• Estimating the dimension of a model
• Multivariate reduced-rank regression
• Asymptotics for Lasso-type estimators.
• Least angle regression. (With discussion)
• Pathwise coordinate optimization
• On the ``degrees of freedom of the lasso
• Biclustering via Sparse Singular Value Decomposition
• Reduced-rank vector generalized linear models
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Asymptotic Statistics
• Sparse Reduced-Rank Regression for Simultaneous Dimension Reduction and Variable Selection
• Reduced rank ridge regression and its kernel extensions
• Bayesian Generalized Low Rank Regression Models for Neuroimaging Phenotypes and Genetic Markers
• Dimension Reduction and Coefficient Estimation in Multivariate Linear Regression
• Regularization and Variable Selection Via the Elastic Net
• Model Selection and Estimation in Regression with Grouped Variables
• The Hartman-Wintner Law of the Iterated Logarithm for Martingales
• Reduced Rank Stochastic Regression with a Sparse Singular value Decomposition
• Estimating Linear Restrictions on Regression Coefficients for Multivariate Normal Distributions
• A selective review of group selection in high-dimensional models

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