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An algorithm for the multivariate group lasso with covariance estimation

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  • I. Wilms
  • C. Croux

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.

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  • I. Wilms & C. Croux, 2018. "An algorithm for the multivariate group lasso with covariance estimation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(4), pages 668-681, March.
    • Handle: RePEc:taf:japsta:v:45:y:2018:i:4:p:668-681
    DOI: 10.1080/02664763.2017.1289503
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    Cited by:

    1. Yang, Yuehan & Xia, Siwei & Yang, Hu, 2023. "Multivariate sparse Laplacian shrinkage for joint estimation of two graphical structures," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).
    2. Zhang, Ruoyang & Ghosh, Malay, 2022. "Ultra high-dimensional multivariate posterior contraction rate under shrinkage priors," Journal of Multivariate Analysis, Elsevier, vol. 187(C).
    3. Bai, Ray & Ghosh, Malay, 2018. "High-dimensional multivariate posterior consistency under global–local shrinkage priors," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 157-170.

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