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An algorithm for the multivariate group lasso with covariance estimation

Journal Contribution - Journal Article

We study a group lasso estimator for the multivariate linear regression model that accounts for correlated error terms. A block coordinate descent algorithm is used to compute this estimator. We perform a simulation study with categorical data and multivariate time series data, typical settings with a natural grouping among the predictor variables. Our simulation studies show the good performance of the proposed group lasso estimator compared to alternative estimators. We illustrate the method on a time series data set of gene expressions.
Journal: Journal of Applied Statistics
ISSN: 0266-4763
Issue: 4
Volume: 45
Pages: 668 - 681
Publication year:2018
BOF-keylabel:yes
IOF-keylabel:yes
BOF-publication weight:0.5
CSS-citation score:1
Authors from:Higher Education
Accessibility:Closed