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E-values have recently emerged as a robust and flexible alternative to p-values for hypothesis testing, especially under optional continuation, i.e., when additional data from further experiments are collected. In this work, we define…

Methodology · Statistics 2025-09-03 Francesca Giuffrida , Diego Garlaschelli , Peter Grünwald

We consider the problem where an active Decision-Maker (DM) is tasked to identify the true hypothesis using as few as possible observations while maintaining accuracy. The DM collects observations according to its determined actions and…

Information Theory · Computer Science 2025-04-29 George Vershinin , Asaf Cohen , Omer Gurewitz

We combine two important ideas in the analysis of large-scale genomics experiments (e.g. experiments that aim to identify genes that are differentially expressed between two conditions). The first is use of Empirical Bayes (EB) methods to…

Methodology · Statistics 2026-02-02 David Gerard , Matthew Stephens

Many decision problems cannot be solved exactly and use several estimation algorithms that assign scores to the different available options. The estimation errors can have various correlations, from low (e.g. between two very different…

Machine Learning · Computer Science 2023-09-06 Theo Delemazure , François Durand , Fabien Mathieu

Heteroskedasticity poses several methodological challenges in designing valid and powerful procedures for simultaneous testing of composite null hypotheses. In particular, the conventional practice of standardizing or re-scaling…

Methodology · Statistics 2025-09-04 Bowen Gang , Trambak Banerjee

In this paper we consider the construction of optimal tests of equivalence hypotheses. Specifically, assume X_1,..., X_n are i.i.d. with distribution P_{\theta}, with \theta \in R^k. Let g(\theta) be some real-valued parameter of interest.…

Statistics Theory · Mathematics 2007-06-13 Joseph P. Romano

In the high dimensional regression analysis when the number of predictors is much larger than the sample size, an important question is to select the important variable which are relevant to the response variable of interest. Variable…

Methodology · Statistics 2023-01-09 Pengsheng Ji , Zhigen Zhao

Multivariate statistics are often available as well as necessary in hypothesis tests. We study how to use such statistics to control not only false discovery rate (FDR) but also positive FDR (pFDR) with good power. We show that FDR can be…

Statistics Theory · Mathematics 2008-05-21 Zhiyi Chi

We collect self-contained elementary proofs of four results in the literature on the false discovery rate of the Benjamini-Hochberg (BH) procedure for independent or positive-regression dependent p-values, the Benjamini-Yekutieli correction…

Statistics Theory · Mathematics 2025-07-15 Ruodu Wang

A general method is presented for deriving the limiting behavior of estimators that are defined as the values of parameters optimizing an empirical criterion function. The asymptotic behavior of such estimators is typically deduced from…

Statistics Theory · Mathematics 2008-12-18 Peter Radchenko

Hypothesis testing via e-variables can be framed as a sequential betting game, where a player each round picks an e-variable. A good player's strategy results in an effective statistical test that rejects the null hypothesis as soon as…

Statistics Theory · Mathematics 2025-05-30 Eugenio Clerico

In the online multiple testing problem, p-values corresponding to different null hypotheses are observed one by one, and the decision of whether or not to reject the current hypothesis must be made immediately, after which the next p-value…

Methodology · Statistics 2017-10-03 Aaditya Ramdas , Fanny Yang , Martin J. Wainwright , Michael I. Jordan

Comparisons are made for the amount of agreement of the composite likelihood information criteria and their full likelihood counterparts when making decisions among the fits of different models, and some properties of penalty term for…

Statistics Theory · Mathematics 2014-10-17 Chi Tim Ng , Harry Joe

Background: The E-value has become widely used for assessing robustness to unmeasured confounding in observational studies, but the original framework was developed for single time-point exposure-outcome settings. This study extends the…

Applications · Statistics 2026-03-02 Md. Niamul Islam Sium

It is often claimed that Bayesian methods, in particular Bayes factor methods for hypothesis testing, can deal with optional stopping. We first give an overview, using elementary probability theory, of three different mathematical meanings…

Statistics Theory · Mathematics 2021-03-24 Allard Hendriksen , Rianne de Heide , Peter Grünwald

Motivation: Combining the results of different experiments to exhibit complex patterns or to improve statistical power is a typical aim of data integration. The starting point of the statistical analysis often comes as sets of p-values…

Methodology · Statistics 2021-12-02 Tristan Mary-Huard , Sarmistha Das , Indranil Mukhopadhyay , Stéphane Robin

This paper introduces a novel conformal selection procedure, inspired by the Neyman--Pearson paradigm, to maximize the power of selecting qualified units while maintaining false discovery rate (FDR) control. Existing conformal selection…

Methodology · Statistics 2025-02-25 Jing Qin , Yukun Liu , Moming Li , Chiung-Yu Huang

We provide a combinatorial derivation of an asymptotic formula for averages of mixed ratios of characteristic polynomials over the unitary group, where mixed ratios are products of ratios and/or logarithmic derivatives. Our proof of this…

Combinatorics · Mathematics 2018-05-21 Helen Riedtmann

We derive inferential procedures for large sample sizes that remain valid under data-dependent significance levels (so-called "post-hoc valid inference"). Classical statistical tools require that the significance level -- the "type-I error"…

Statistics Theory · Mathematics 2026-03-10 Ben Chugg , Etienne Gauthier , Michael I. Jordan , Aaditya Ramdas , Ian Waudby-Smith

Possibilistic and qualitative POMDPs (pi-POMDPs) are counterparts of POMDPs used to model situations where the agent's initial belief or observation probabilities are imprecise due to lack of past experiences or insufficient data…

Artificial Intelligence · Computer Science 2013-09-27 Nicolas Drougard , Florent Teichteil-Konigsbuch , Jean-Loup Farges , Didier Dubois
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