5 citations · 15 across the 7 of their papers we have counts for
8 papers
Response-guided knockoffs for directional FDR control in linear models
Jack Freestone, Garth Tarr, Samuel Muller +1
We consider the problem of feature selection in linear models with finite-sample control of the false discovery rate (FDR). While existing knockoff-based methods control the direct…
A semi-supervised framework for diverse multiple hypothesis testing scenarios
Jack Freestone, William Stafford Noble, Uri Keich
Standard multiple testing procedures are designed to report a list of discoveries, or suspected false null hypotheses, given the hypotheses' p-values or test scores. Recently there…
Bounding the FDP in competition-based control of the FDR
Arya Ebadi, Dong Luo, Jack Freestone +2
Competition-based approach to controlling the false discovery rate (FDR) recently rose to prominence when, generalizing it to sequential hypothesis testing, Barber and Candès used…
Controlling the False Discovery Rate via Competition: is the +1 needed?
Andrew Rajchert, Uri Keich
Barber and Candès (2015) control of the FDR in feature selection relies on estimating the FDR by the number of knockoff wins +1 divided by the number of original wins. We study the…
Competition-based control of the false discovery proportion
Dong Luo, Arya Ebadi, Yilun He +3
Recently, Barber and Candès laid the theoretical foundation for a general framework for false discovery rate (FDR) control based on the notion of "knockoffs." A closely related FDR…
Exactly computing the tail of the Poisson-Binomial Distribution
Noah Peres, Andrew Lee, Uri Keich
We offer ShiftConvolvePoibin, a fast exact method to compute the tail of a Poisson-Binomial distribution (PBD). Our method employs an exponential shift to retain its accuracy when…