Lie Wang coauthored the Square-root Lasso paper with Alexandre Belloni and Victor Chernozhukov. Their estimator combines a root-mean-square residual loss with an penalty. Its tuning rule avoids requiring advance knowledge of the noise standard deviation, making sparse linear regression feasible when the number of candidate predictors exceeds the number of observations.[ref]
Square-Root Lasso: Pivotal Recovery of Sparse Signals via Conic Programming by Alexandre Belloni and others. Section 1, equations 8–11; exact author byline
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