= Lie Wang
{c}
<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 $\ell^1$ 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.<Square-Root Lasso: Pivotal Recovery of Sparse Signals via Conic Programming by Alexandre Belloni and others>[Section 1, equations 8–11; exact author byline]{ref}
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