166 citations · 424 across the 44 of their papers we have counts for
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stat.ME2013★ 22 cited
Trust, but verify: benefits and pitfalls of least-squares refitting in high dimensions
Johannes Lederer
Least-squares refitting is widely used in high dimensional regression to reduce the prediction bias of l1-penalized estimators (e.g., Lasso and Square-Root Lasso). We present theor…
math.ST2013★ 9 cited
The Group Square-Root Lasso: Theoretical Properties and Fast Algorithms
Florentina Bunea, Johannes Lederer, Yiyuan She
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 s…