65 citations · 86 across the 27 of their papers we have counts for
28 papers
Empirical Bayes prepivoting under group invariance: false discovery rate control and moderated t-tests
Nikolaos Ignatiadis, Etienne Roquain
We consider simultaneously testing hypotheses about thousands of units, e.g., genes or proteins, where each unit yields a handful of replicate measurements and we test whether its…
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…
Normal approximations in nonparametric empirical Bayes
Jiafeng Chen, Nabarun Deb, Nikolaos Ignatiadis
Empirical Bayes analyses routinely model noisy measurements of latent parameters as normal, justifying this by an informal appeal to the central limit theorem (CLT). This paper put…
How does limma-trend work? An empirical partially Bayes perspective
Sagnik Nandy, Wanyi Ling, Nikolaos Ignatiadis
In high-throughput biology, it is common to fit thousands of linear regressions -- one per gene, protein, or other unit -- with very few samples per unit. Limma-trend, one of the m…
Empirical Bayes Rebiasing
Wanyi Ling, Sida Li, Junming Guan +1
We study methods for simultaneous analysis of many noisy and biased estimates, each paired with an even noisier estimate of its own bias. The analyst's goal is to construct short c…
Tiny but uniform improvements of adaptive BH procedures via compound e-values
Nikolaos Ignatiadis, Ruodu Wang, Aaditya Ramdas
After the seminal Benjamini--Hochberg (BH) procedure for controlling the false discovery rate (FDR) was proposed, dozens of papers have attempted to improve its power by adapting t…
Compound decisions and empirical Bayes via Bayesian nonparametrics
Nikolaos Ignatiadis, Sid Kankanala
We study compound decision theory from a nonparametric Bayesian perspective, with particular emphasis on their relationship to empirical Bayes (EB) procedures. Motivated by the sha…
Partially Bayes p-values for large scale inference
Nikolaos Ignatiadis, Li Ma
We seek to conduct statistical inference for a large collection of primary parameters, each with its own nuisance parameters. Our approach is partially Bayesian, in that we treat t…
Empirical Bayes learning from selectively reported confidence intervals
Hunter Chen, Junming Guan, Erik van Zwet +1
We develop a statistical framework for empirical Bayes learning from selectively reported confidence intervals, and apply it to provide context for interpreting results published i…
Empirical partially Bayes two sample testing
Wanyi Ling, Wufang Hong, Nikolaos Ignatiadis
A common task in high-throughput biology is to test for differences in means between two samples across thousands of features (e.g., genes or proteins), often with only a handful o…
Reasonable uncertainty: Confidence intervals in empirical Bayes discrimination detection
Jiaying Gu, Nikolaos Ignatiadis, Azeem M. Shaikh
We revisit empirical Bayes discrimination detection, focusing on uncertainty arising from both partial identification and sampling variability. While prior work has mostly focused…
Constrained Denoising, Empirical Bayes, and Optimal Transport
Adam Quinn Jaffe, Nikolaos Ignatiadis, Bodhisattva Sen
In latent variables models, two important goals are denoising and deconvolution: denoising aims to estimate the latent variables, whereas deconvolution aims to estimate the distrib…
Stein's unbiased risk estimate and Hyvärinen's score matching
Sulagna Ghosh, Nikolaos Ignatiadis, Frederic Koehler +1
Given a collection of observed signals corrupted with Gaussian noise, how can we learn to optimally denoise them? This fundamental problem arises in both empirical Bayes and genera…
Prediction-Powered Adaptive Shrinkage Estimation
Sida Li, Nikolaos Ignatiadis
Prediction-Powered Inference (PPI) is a powerful framework for enhancing statistical estimates by combining limited gold-standard data with machine learning (ML) predictions. While…
Empirical Bayes estimation via data fission
Nikolaos Ignatiadis, Dennis L. Sun
We demonstrate how data fission, a method for creating synthetic replicates from single observations, can be applied to empirical Bayes estimation. This extends recent work on empi…
Asymptotic and compound e-values: multiple testing and empirical Bayes
Nikolaos Ignatiadis, Ruodu Wang, Aaditya Ramdas
We explicitly define the notions of (bona fide, approximate or asymptotic) compound p-values and e-values, which have been implicitly presented and used in the recent multiple test…
Interpretation of local false discovery rates under the zero assumption
Daniel Xiang, Nikolaos Ignatiadis, Peter McCullagh
In large-scale studies with parallel signal-plus-noise observations, the local false discovery rate is a summary statistic that is often presumed to be equal to the posterior proba…
Empirical partially Bayes multiple testing and compound decisions
Nikolaos Ignatiadis, Bodhisattva Sen
A common task in high-throughput biology is to screen for associations across thousands of units of interest, e.g., genes or proteins. Often, the data for each unit are modeled as…
Treatment Heterogeneity for Survival Outcomes
Yizhe Xu, Nikolaos Ignatiadis, Erik Sverdrup +3
Estimation of conditional average treatment effects (CATEs) plays an essential role in modern medicine by informing treatment decision-making at a patient level. Several metalearne…
E-values as unnormalized weights in multiple testing
Nikolaos Ignatiadis, Ruodu Wang, Aaditya Ramdas
We study how to combine p-values and e-values, and design multiple testing procedures where both p-values and e-values are available for every hypothesis. Our results provide a new…
Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare
Stephen R. Pfohl, Yizhe Xu, Agata Foryciarz +3
A growing body of work uses the paradigm of algorithmic fairness to frame the development of techniques to anticipate and proactively mitigate the introduction or exacerbation of h…
-Ridge: group regularized ridge regression via empirical Bayes noise level cross-validation
Nikolaos Ignatiadis, Panagiotis Lolas
Features in predictive models are not exchangeable, yet common supervised models treat them as such. Here we study ridge regression when the analyst can partition the features into…
Using public clinical trial reports to evaluate observational study methods
Ethan Steinberg, Nikolaos Ignatiadis, Steve Yadlowsky +2
Observational studies are valuable for estimating the effects of various medical interventions, but are notoriously difficult to evaluate because the methods used in observational…
Noise-Induced Randomization in Regression Discontinuity Designs
Dean Eckles, Nikolaos Ignatiadis, Stefan Wager +1
Regression discontinuity designs assess causal effects in settings where treatment is determined by whether an observed running variable crosses a pre-specified threshold. Here we…