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Econometric applications with multi-way clustering often feature a small number of effective clusters or heavy-tailed data, making standard cluster-robust and bootstrap inference unreliable in finite samples. In this paper, we develop a…

Methodology · Statistics 2026-01-14 Wenxuan Guo , Panos Toulis , Yuhao Wang

Forecasting and forecast evaluation are inherently sequential tasks. Predictions are often issued on a regular basis, such as every hour, day, or month, and their quality is monitored continuously. However, the classical statistical tools…

Methodology · Statistics 2022-07-04 Sebastian Arnold , Alexander Henzi , Johanna F. Ziegel

This paper studies the high-dimensional mixed linear regression (MLR) where the output variable comes from one of the two linear regression models with an unknown mixing proportion and an unknown covariance structure of the random…

Methodology · Statistics 2020-11-10 Linjun Zhang , Rong Ma , T. Tony Cai , Hongzhe Li

Compared to p-values, e-values provably guarantee safe, valid inference. If the goal is to test multiple hypotheses simultaneously, one can construct e-values for each individual test and then use the recently developed e-BH procedure to…

Methodology · Statistics 2024-12-03 Neil Dey , Ryan Martin , Jonathan P. Williams

Phase retrieval has been an attractive but difficult problem rising from physical science, and there has been a gap between state-of-the-art theoretical convergence analyses and the corresponding efficient retrieval methods. Firstly, these…

Information Theory · Computer Science 2017-12-06 Gen Li , Yuchen Jiao , Yuantao Gu

Constraint-based causal discovery relies on numerous conditional independence tests (CITs), but its practical applicability is severely constrained by the prohibitive computational cost, especially as CITs themselves have high time…

Machine Learning · Computer Science 2026-03-02 Zhengkang Guan , Kun Kuang

Stepped-wedge cluster randomised trials (SW-CRTs) increasingly evaluate complex interventions, yet methodological guidance for analysing composite endpoints using generalized pairwise comparisons (GPC)remains limited. This work investigates…

We present a novel necessary and sufficient principle for False Discovery Rate (FDR) control. This e-Partitioning Principle says that a procedure controls FDR if and only if it is a special case of a general e-Partitioning procedure. By…

Statistics Theory · Mathematics 2025-09-15 Jelle Goeman , Rianne de Heide , Aldo Solari

Testing the association between a phenotype and many genetic variants from case-control data is essential in genome-wide association study (GWAS). This is a challenging task as many such variants are correlated or non-informative.…

Methodology · Statistics 2016-01-25 Zhendong Huang , Davide Ferrari , Guoqi Qian

There is a useful counterpart of conformal prediction for e-values, called conformal e-prediction. Conformal prediction can serve as basis for testing the assumption of exchangeability, leading to conformal testing. Similarly, conformal…

Statistics Theory · Mathematics 2024-11-05 Vladimir Vovk , Ilia Nouretdinov , Alex Gammerman

There has been a misconception that only one type of error rate control is necessary in clinical trials, leading to debates over whether to prioritize Familywise Error Rate (FWER) or False Discovery Rate (FDR). This misconception has led to…

Methodology · Statistics 2026-03-26 Xinping Cui , Emily Ouyang , Yi Liu , Jingjing Yan Schneider , Hong Tian , Bushi Wang , Jason C. Hsu

Motivation: Gene set testing is typically performed in a supervised context to quantify the association between groups of genes and a clinical phenotype. In many cases, however, a gene set-based interpretation of genomic data is desired in…

Quantitative Methods · Quantitative Biology 2015-03-17 H. Robert Frost , Zhigang Li , Jason H. Moore

We provide an approach to exploratory data analysis in matched observational studies with a single intervention and multiple endpoints. In such settings, the researcher would like to explore evidence for actual treatment effects among these…

Methodology · Statistics 2025-12-10 Mengqi Lin , Colin Fogarty

Hypothesis testing is a statistical method used to draw conclusions about populations from sample data, typically represented in tables. With the prevalence of graph representations in real-life applications, hypothesis testing in graphs is…

Machine Learning · Statistics 2025-02-27 Yun Wang , Chrysanthi Kosyfaki , Sihem Amer-Yahia , Reynold Cheng

The standard constraint-based paradigm for causal discovery with incomplete data -- impute first, test second -- is frequently miscalibrated: any consistent conditional independence (CI) test rejects a true null with probability approaching…

Methodology · Statistics 2026-05-07 Thomas S. Robinson , Ranjit Lall

Genetic Gaussian network of multiple phenotypes constructed through the genetic correlation matrix is informative for understanding their biological dependencies. However, its interpretation may be challenging because the estimated genetic…

Methodology · Statistics 2024-12-31 Yihe Yang , Noah Lorincz-Comi , Xiaofeng Zhu

Both cluster randomized trials and quasi-experimental designs are used to evaluate the impact of health and social policies and interventions. Stepped-wedge cluster randomized trials randomize a staggered adoption approach, while recent…

Methodology · Statistics 2026-04-15 Haidong Lu , Gregg S. Gonsalves , Fan Li , Guanyu Tong , Lee Kennedy-Shaffer

Genetic variants in cis-regulatory elements or trans-acting regulators commonly influence the quantity and spatiotemporal distribution of gene transcription. Recent interest in expression quantitative trait locus (eQTL) mapping has…

Genomics · Quantitative Biology 2015-11-12 Christopher D Brown , Lara M Mangravite , Barbara E Engelhardt

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 of replicates per sample. Moderated t-tests handle this…

Methodology · Statistics 2025-10-02 Wanyi Ling , Wufang Hong , Nikolaos Ignatiadis

The problem of combining p-values is an old and fundamental one, and the classic assumption of independence is often violated or unverifiable in many applications. There are many well-known rules that can combine a set of arbitrarily…

Statistics Theory · Mathematics 2025-03-21 Matteo Gasparin , Ruodu Wang , Aaditya Ramdas