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

@article{Bunea2014TheGS,
  title={The Group Square-Root Lasso: Theoretical Properties and Fast Algorithms},
  author={Florentina Bunea and Johannes Lederer and Yiyuan She},
  journal={IEEE Transactions on Information Theory},
  year={2014},
  volume={60},
  pages={1313-1325}
}
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… CONTINUE READING
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