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An algorithm for the multivariate group lasso with covariance estimation

Tijdschriftbijdrage - Tijdschriftartikel

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.
Tijdschrift: Journal of Applied Statistics
ISSN: 0266-4763
Issue: 4
Volume: 45
Pagina's: 668 - 681
Jaar van publicatie:2018