Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Ignatiadis, Nikolaos"'
Autor:
Ignatiadis, Nikolaos, Sun, Dennis L.
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 empirical Bayes with multiple replicates to the classical single-replicate
Externí odkaz:
http://arxiv.org/abs/2410.12117
We explicitly define the notion of (exact or approximate) compound e-values which have been implicitly presented and extensively used in the recent multiple testing literature. We show that every FDR controlling procedure can be recovered by instanti
Externí odkaz:
http://arxiv.org/abs/2409.19812
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 probability that the signal is null. We prefer to call the latter quantity
Externí odkaz:
http://arxiv.org/abs/2402.08792
Autor:
Ignatiadis, Nikolaos, Sen, Bodhisattva
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 Gaussian measurements with unknown mean and variance and are summarize
Externí odkaz:
http://arxiv.org/abs/2303.02887
Estimation of conditional average treatment effects (CATEs) plays an essential role in modern medicine by informing treatment decision-making at a patient level. Several metalearners have been proposed recently to estimate CATEs in an effective and f
Externí odkaz:
http://arxiv.org/abs/2207.07758
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 perspective on multiple testing with data-driven weights: while stand
Externí odkaz:
http://arxiv.org/abs/2204.12447
Autor:
Pfohl, Stephen R., Xu, Yizhe, Foryciarz, Agata, Ignatiadis, Nikolaos, Genkins, Julian, Shah, Nigam H.
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 health inequities that may follow from the use of model-guided decision
Externí odkaz:
http://arxiv.org/abs/2202.01906
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 $K$ groups based on external side-information. For example, in high-t
Externí odkaz:
http://arxiv.org/abs/2010.15817
Observational studies are valuable for estimating the effects of various medical interventions, but are notoriously difficult to evaluate because the methods used in observational studies require many untestable assumptions. This lack of verifiabilit
Externí odkaz:
http://arxiv.org/abs/2006.14102
Regression discontinuity designs are used to estimate causal effects in settings where treatment is determined by whether an observed running variable crosses a pre-specified threshold. While the resulting sampling design is sometimes described as ak
Externí odkaz:
http://arxiv.org/abs/2004.09458