An algorithm for the multivariate group lasso with covariance estimation
Computation
2015-12-17 v1 Methodology
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.
Cite
@article{arxiv.1512.05153,
title = {An algorithm for the multivariate group lasso with covariance estimation},
author = {Ines Wilms and Christophe Croux},
journal= {arXiv preprint arXiv:1512.05153},
year = {2015}
}