16 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…
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 to…
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…