An algorithm for the multivariate group lasso with covariance estimation. Issue 4 (12th March 2018)

• Record Type: Journal Article
• Authors: Wilms, I.; Croux, C.
• Is Part Of: Journal of applied statistics. Volume 45:Issue 4(2018)
• Page Start: 668
• Page End: 681
• Publication Date: 2018-03-12
• Subjects: Categorical variables -- group lasso -- multivariate regression -- penalized maximum likelihood -- sparsity -- time series; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2017.1289503
• Languages: English
• ISSNs: 0266-4763

Abstract

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.