285 citations · 784 across the 27 of their papers we have counts for
30 papers
Estimating the local false discovery rate under an unknown symmetric null
Daniel Xiang, William Fithian, Nikolaos Ignatiadis +2
This paper is concerned with estimating the local false discovery rate (lfdr) in a two-groups model where the only assumption regarding the null distribution is symmetry about zero…
A frequentist local false discovery rate
Daniel Xiang, Jake A. Soloff, William Fithian
The local false discovery rate (lfdr) of Efron et al. (2001) enjoys major conceptual and decision-theoretic advantages over the false discovery rate (FDR) as an error criterion in…
A Flexible Defense Against the Winner's Curse
Tijana Zrnic, William Fithian
Across science and policy, decision-makers often need to draw conclusions about the best candidate among competing alternatives. For instance, researchers may seek to infer the eff…
Estimating the False Discovery Rate of Variable Selection
Yixiang Luo, William Fithian, Lihua Lei
We introduce a generic estimator for the false discovery rate of any model selection procedure, in common statistical modeling settings including the Gaussian linear model, Gaussia…
Locally Simultaneous Inference
Tijana Zrnic, William Fithian
Selective inference is the problem of giving valid answers to statistical questions chosen in a data-driven manner. A standard solution to selective inference is simultaneous infer…
Asymptotically Optimal Knockoff Statistics via the Masked Likelihood Ratio
Asher Spector, William Fithian
In feature selection problems, knockoffs are synthetic controls for the original features. Employing knockoffs allows analysts to use nearly any variable importance measure or "fea…
Improving knockoffs with conditional calibration
Yixiang Luo, William Fithian, Lihua Lei
The knockoff filter of Barber and Candes (arXiv:1404.5609) is a flexible framework for multiple testing in supervised learning models, based on introducing synthetic predictor vari…
The edge of discovery: Controlling the local false discovery rate at the margin
Jake A. Soloff, Daniel Xiang, William Fithian
Despite the popularity of the false discovery rate (FDR) as an error control metric for large-scale multiple testing, its close Bayesian counterpart the local false discovery rate…
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…
Statistical Methods for Replicability Assessment
Kenneth Hung, William Fithian
Large-scale replication studies like the Reproducibility Project: Psychology (RP:P) provide invaluable systematic data on scientific replicability, but most analyses and interpreta…
Family learning: nonparametric statistical inference with parametric efficiency
William Fithian, Daniel Ting
Hypothesis testing and other statistical inference procedures are most efficient when a reliable low-dimensional parametric family can be specified. We propose a method that learns…
STAR: A general interactive framework for FDR control under structural constraints
Lihua Lei, Aaditya Ramdas, William Fithian
We propose a general framework based on selectively traversed accumulation rules (STAR) for interactive multiple testing with generic structural constraints on the rejection set. I…
Rank Verification for Exponential Families
Kenneth Hung, William Fithian
Many statistical experiments involve comparing multiple population groups. For example, a public opinion poll may ask which of several political candidates commands the most suppor…
AdaPT: An interactive procedure for multiple testing with side information
Lihua Lei, William Fithian
We consider the problem of multiple hypothesis testing with generic side information: for each hypothesis we observe both a p-value and some predictor encoding co…
Power of Ordered Hypothesis Testing
Lihua Lei, William Fithian
Ordered testing procedures are multiple testing procedures that exploit a pre-specified ordering of the null hypotheses, from most to least promising. We analyze and compare the po…
Multiple Correspondence Analysis & the Multilogit Bilinear Model
William Fithian, Julie Josse
Multiple Correspondence Analysis (MCA) is a dimension reduction method which plays a large role in the analysis of tables with categorical nominal variables such as survey data. Th…
Data Augmentation via Levy Processes
Stefan Wager, William Fithian, Percy Liang
If a document is about travel, we may expect that short snippets of the document should also be about travel. We introduce a general framework for incorporating these types of inva…
Selective Sequential Model Selection
William Fithian, Jonathan Taylor, Robert Tibshirani +1
Many model selection algorithms produce a path of fits specifying a sequence of increasingly complex models. Given such a sequence and the data used to produce them, we consider th…
Optimal Inference After Model Selection
William Fithian, Dennis Sun, Jonathan Taylor
To perform inference after model selection, we propose controlling the selective type I error; i.e., the error rate of a test given that it was performed. By doing so, we recover l…