Abstract
We propose a ranking and selection procedure to prioritize relevant predictors and control false discovery proportion (FDP) in variable selection. Our procedure utilizes a new ranking method built upon the de-sparsified Lasso estimator. We show that the new ranking method achieves the optimal order of minimum non-zero effects in ranking relevant predictors ahead of irrelevant ones. Adopting the new ranking method, we develop a variable selection procedure to asymptotically control FDP at a user-specified level. We show that our procedure can consistently estimate the FDP of variable selection as long as the de-sparsified Lasso estimator is asymptotically normal. In simulations, our procedure compares favorably to existing methods in ranking efficiency and FDP control when the regression model is relatively sparse.
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