NLNR localizes inference for each target coefficient to a low-dimensional regression on its sparse conditional neighborhood, yielding consistent and asymptotically normal estimators under regularity conditions.
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Principled Inference in Dense High-Dimensional Linear Models via Local Conditional Sparsity
NLNR localizes inference for each target coefficient to a low-dimensional regression on its sparse conditional neighborhood, yielding consistent and asymptotically normal estimators under regularity conditions.