A stability-derived update formula for a generalized debiased Lasso yields asymptotically accurate approximations for most coordinates under sub-Gaussian designs in the proportional regime, enabling faster resampling-based variable selection.
On the impact of predictor geometry on the performance on high-dimensional ridge-regularized generalized robust regression estimators
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Stability of a Generalized Debiased Lasso with Applications to Resampling-Based Variable Selection
A stability-derived update formula for a generalized debiased Lasso yields asymptotically accurate approximations for most coordinates under sub-Gaussian designs in the proportional regime, enabling faster resampling-based variable selection.