# The Group Square-Root Lasso: Theoretical Properties and Fast Algorithms

@article{Bunea2014TheGS,
title={The Group Square-Root Lasso: Theoretical Properties and Fast Algorithms},
author={F. Bunea and Johannes Lederer and Y. She},
journal={IEEE Transactions on Information Theory},
year={2014},
volume={60},
pages={1313-1325}
}
• Published 2014
• Mathematics, Computer Science
• IEEE Transactions on Information Theory
We introduce and study the group square-root lasso (GSRL) method for estimation in high dimensional sparse regression models with group structure. The new estimator minimizes the square root of the residual sum of squares plus a penalty term proportional to the sum of the Euclidean norms of groups of the regression parameter vector. The net advantage of the method over the existing group lasso-type procedures consists in the form of the proportionality factor used in the penalty term, which for… Expand
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