An algorithm for the multivariate group lasso with covariance estimation

Ines Wilms and C. Croux

Journal of Applied Statistics, 2018, vol. 45, issue 4, 668-681

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.

Date: 2018
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Working Paper: An algorithm for the multivariate group lasso with covariance estimation (2015)
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DOI: 10.1080/02664763.2017.1289503

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