An algorithm for the multivariate group lasso with covariance estimation

Ines Wilms and Christophe Croux

No 516983, Working Papers of Department of Decision Sciences and Information Management, Leuven from KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven

Abstract: 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.

Keywords: Categorical variables; Group Lasso; Multivariate Regression; Penalized Maximum Likelihood; Sparsity; Time Series (search for similar items in EconPapers)
Date: 2015-11
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Published in FEB Research Report KBI_1528

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Journal Article: An algorithm for the multivariate group lasso with covariance estimation (2018)
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