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.
Hypothesis testing in high-dimensional regression under the gaussian random design model: Asymptotic theory
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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.