An algorithm for the multivariate group lasso with covariance estimation

DOI10.1080/02664763.2017.1289503OpenAlexW2204185640MaRDI QIDQ5139028

Ines Wilms, Christophe Croux

Publication date: 4 December 2020

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

Full work available at URL: https://arxiv.org/abs/1512.05153

zbMATH Keywords

time seriessparsitygroup Lassomultivariate regressioncategorical variablespenalized maximum likelihood

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (6)

High-dimensional multivariate posterior consistency under global-local shrinkage priors ⋮ Regularized estimation in sparse high-dimensional multivariate regression, with application to a DNA methylation study ⋮ The EAS approach to variable selection for multivariate response data in high-dimensional settings ⋮ Capturing between-tasks covariance and similarities using multivariate linear mixed models ⋮ Ultra high-dimensional multivariate posterior contraction rate under shrinkage priors ⋮ Multivariate sparse Laplacian shrinkage for joint estimation of two graphical structures

Uses Software

• glasso
• selectiveInference

Cites Work

• Unnamed Item
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• Statistics for high-dimensional data. Methods, theory and applications.
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• Sparse permutation invariant covariance estimation
• Pathwise coordinate optimization
• Sparse reduced-rank regression with covariance estimation
• Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure
• A study of error variance estimation in Lasso regression
• Model selection and estimation in the Gaussian graphical model
• The Group Lasso for Logistic Regression
• Sparse group variable selection based on quantile hierarchical Lasso
• Construction of disease risk scoring systems using logistic group lasso: application to porcine reproductive and respiratory syndrome survey data
• Model Selection and Estimation in Regression with Grouped Variables
• Ridge Regression: Biased Estimation for Nonorthogonal Problems

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