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Activity Number: 187 - Contributed Poster Presentations: Section on Nonparametric Statistics
Type: Contributed
Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #308011
Title: Generating Knockoffs Without Knowing the Distributions of the Covariates
Author(s): Dongming Huang*
Companies: Harvard University

A recent framework called model-X knockoffs performs variable selection while non-asymptotically controls the false discovery rate with no restrictions or assumptions on the dimensionality of the data or the conditional distribution of the response given the covariates. The one requirement for the procedure is that the covariate samples are drawn independently and identically from a precisely-known (but arbitrary) distribution. The present paper shows that the exact same guarantees can be made without knowing the covariate distribution fully, but instead knowing it only up to a parametric model that can have number of parameters at the order of $np$, the number of observations times the number of variables. We demonstrate how to do this for 3 models of interest, with simulations showing the new approach remains powerful under the weaker assumptions.

Authors who are presenting talks have a * after their name.

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