4 citations · 13 across the 5 of their papers we have counts for
10 papers
BONuS: Multiple multivariate testing with a data-adaptivetest statistic
Chiao-Yu Yang, Lihua Lei, Nhat Ho +1
We propose a new adaptive empirical Bayes framework, the Bag-Of-Null-Statistics (BONuS) procedure, for multiple testing where each hypothesis testing problem is itself multivariate…
Whiteout: when do fixed-X knockoffs fail?
Xiao Li, William Fithian
A core strength of knockoff methods is their virtually limitless customizability, allowing an analyst to exploit machine learning algorithms and domain knowledge without threatenin…
AdaPT-GMM: Powerful and robust covariate-assisted multiple testing
Patrick Chao, William Fithian
We propose a new empirical Bayes method for covariate-assisted multiple testing with false discovery rate (FDR) control, where we model the local false discovery rate for each hypo…
Conditional calibration for false discovery rate control under dependence
William Fithian, Lihua Lei
We introduce a new class of methods for finite-sample false discovery rate (FDR) control in multiple testing problems with dependent test statistics where the dependence is fully o…
Optimality of the max test for detecting sparse signals with Gaussian or heavier tail
Xiao Li, William Fithian
A fundamental problem in high-dimensional testing is that of global null testing: testing whether the null holds simultaneously in all of hypotheses. The max test, which uses t…
Smoothed Nested Testing on Directed Acyclic Graphs
Jackson H. Loper, Lihua Lei, William Fithian +1
We consider the problem of multiple hypothesis testing when there is a logical nested structure to the hypotheses. When one hypothesis is nested inside another, the outer hypothesi…