Tootfinder

This https://arxiv.org/abs/2411.16666 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

CatNet: Controlling the False Discovery Rate in LSTM with SHAP Feature Importance and Gaussian Mirrors
We introduce CatNet, an algorithm that effectively controls False Discovery Rate (FDR) and selects significant features in LSTM. CatNet employs the derivative of SHAP values to quantify the feature importance, and constructs a vector-formed mirror statistic for FDR control with the Gaussian Mirror algorithm. To avoid instability due to nonlinear or temporal correlations among features, we also propose a new kernel-based independence measure. CatNet performs robustly on different model settings …

Today Man blames Yesterday Man for his problems and thinks Tomorrow Man will save him.
- some damn FDR thing

Gold after Randomized Sand: Model-X Split Knockoffs for Controlled Transformation Selection
Yang Cao, Hangyu Lin, Xinwei Sun, Yuan Yao
https://arxiv.org/abs/2507.01732

Gold after Randomized Sand: Model-X Split Knockoffs for Controlled Transformation Selection
Controlling the False Discovery Rate (FDR) in variable selection is crucial for reproducibility and preventing over-selection, particularly with the increasing prevalence of predictive modeling. The Split Knockoff method, a recent extension of the canonical Knockoffs framework, offers finite-sample FDR control for selecting sparse transformations, finding applications across signal processing, economics, information technology, and the life sciences. However, its current formulation is limited …

But the Democratic party still at heart seeks to be a progressive party in the FDR/LBJ mold. The party’s gambit since Nixon has been that an FDR-shaped coalition would force the fascist-shape coalition to fracture. It looked like that might even be true for a few of the Obama years. Sadly, nope.
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e-GAI: e-value-based Generalized $\alpha$-Investing for Online False Discovery Rate Control
Yifan Zhang, Zijian Wei, Haojie Ren, Changliang Zou
https://arxiv.org/abs/2506.01452

e-GAI: e-value-based Generalized $α$-Investing for Online False Discovery Rate Control
Online multiple hypothesis testing has attracted a lot of attention in many applications, e.g., anomaly status detection and stock market price monitoring. The state-of-the-art generalized $α$-investing (GAI) algorithms can control online false discovery rate (FDR) on p-values only under specific dependence structures, a situation that rarely occurs in practice. The e-LOND algorithm (Xu & Ramdas, 2024) utilizes e-values to achieve online FDR control under arbitrary dependence but suffers from …

This https://arxiv.org/abs/2505.20688 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

A False Discovery Rate Control Method Using a Fully Connected Hidden Markov Random Field for Neuroimaging Data
False discovery rate (FDR) control methods are essential for voxel-wise multiple testing in neuroimaging data analysis, where hundreds of thousands or even millions of tests are conducted to detect brain regions associated with disease-related changes. Classical FDR control methods (e.g., BH, q-value, and LocalFDR) assume independence among tests and often lead to high false non-discovery rates (FNR). Although various spatial FDR control methods have been developed to improve power, they still …

False discovery rate control with compound p-values
Rina Foygel Barber, Richard J Samworth
https://arxiv.org/abs/2507.21465 https://arxiv.org/pdf/2507.2146…

False discovery rate control with compound p-values
In the setting of multiple testing, compound p-values generalize p-values by asking for superuniformity to hold only \emph{on average} across all true nulls. We study the properties of the Benjamini--Hochberg procedure applied to compound p-values. Under independence, we show that the false discovery rate (FDR) is at most $1.93α$, where $α$ is the nominal level, and exhibit a distribution for which the FDR is $\frac{7}{6}α$. If additionally all nulls are true, then the upper bound can be imp…

Nullstrap-DE: A General Framework for Calibrating FDR and Preserving Power in DE Methods, with Applications to DESeq2 and edgeR
Chenxin Jiang, Changhu Wang, Jingyi Jessica Li
https://arxiv.org/abs/2507.20598

Nullstrap-DE: A General Framework for Calibrating FDR and Preserving Power in DE Methods, with Applications to DESeq2 and edgeR
Differential expression (DE) analysis is a key task in RNA-seq studies, aiming to identify genes with expression differences across conditions. A central challenge is balancing false discovery rate (FDR) control with statistical power. Parametric methods such as DESeq2 and edgeR achieve high power by modeling gene-level counts using negative binomial distributions and applying empirical Bayes shrinkage. However, these methods may suffer from FDR inflation when model assumptions are mildly viola…

COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees
Zhiyuan Wang, Jinhao Duan, Qingni Wang, Xiaofeng Zhu, Tianlong Chen, Xiaoshuang Shi, Kaidi Xu
https://arxiv.org/abs/2506.20178

COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees
Uncertainty quantification (UQ) for foundation models is essential to identify and mitigate potential hallucinations in automatically generated text. However, heuristic UQ approaches lack formal guarantees for key metrics such as the false discovery rate (FDR) in selective prediction. Previous work adopts the split conformal prediction (SCP) framework to ensure desired coverage of admissible answers by constructing prediction sets, but these sets often contain incorrect candidates, limiting the…

FDR delivered his fireside chats in a Donald Duck voice.
#LowQualityFacts

Multiple Hypothesis Testing in Genomics
Shyam Gupta
https://arxiv.org/abs/2506.23428 https://arxiv.org/pdf/2506.23428

Multiple Hypothesis Testing in Genomics
This analysis report presents an in-depth exploration of multiple hypothesis testing in the context of Genomics RNA-seq differential expression (DE) analysis, with a primary focus on techniques designed to control the false discovery rate (FDR). While RNA-seq has become a cornerstone in transcriptomic research, accurately detecting expression changes remains challenging due to the high-dimensional nature of the data. This report delves into the Benjamini-Hochberg (BH) procedure, Benjamini-Yekut…

A powerful procedure that controls the false discovery rate with directional information
Zhaoyang Tian, Kun Liang, Pengfei Li
https://arxiv.org/abs/2507.15631

A powerful procedure that controls the false discovery rate with directional information
In many multiple testing applications in genetics, the signs of test statistics provide useful directional information, such as whether genes are potentially up- or down-regulated between two experimental conditions. However, most existing procedures that control the false discovery rate (FDR) are $p$-value based and ignore such directional information. We introduce a novel procedure, the signed-knockoff procedure, to utilize the directional information and control the FDR in finite samples. We…

Inference for microbe--metabolite association networks using a latent graph model
Jing Ma
https://arxiv.org/abs/2506.12275 https://ar…

Inference for microbe--metabolite association networks using a latent graph model
Correlation networks are commonly used to infer associations between microbes and metabolites. The resulting p-values are then corrected for multiple comparisons using existing methods such as the Benjamini and Hochberg procedure to control the false discovery rate (FDR). However, most existing methods for FDR control assume the p-values are weakly dependent. Consequently, they can have low power in recovering microbe-metabolite association networks that exhibit important topological features, …

FDR controlling procedures with dimension reduction and their application to GWAS with linkage disequilibrium score
Dayeon Jung, Yewon Kim, Junyong Park
https://arxiv.org/abs/2507.06049

FDR controlling procedures with dimension reduction and their application to GWAS with linkage disequilibrium score
Genome-wide association studies (GWAS) have led to the discovery of numerous single nucleotide polymorphisms (SNPs) associated with various phenotypes and complex diseases. However, the identified genetic variants do not fully explain the heritability of complex traits, known as the missing heritability problem. To address this challenge and accurately control false positives while maximizing true associations, we propose two approaches involving linkage disequilibrium (LD) scores as covariates…

Knockoffs Inference under Privacy Constraints
Zhanrui Cai, Yingying Fan, Lan Gao
https://arxiv.org/abs/2506.09690 https://arxiv.org/p…

Knockoffs Inference under Privacy Constraints
Model-X knockoff framework offers a model-free variable selection method that ensures finite sample false discovery rate (FDR) control. However, the complexity of generating knockoff variables, coupled with the model-free assumption, presents significant challenges for protecting data privacy in this context. In this paper, we propose a comprehensive framework for knockoff inference within the differential privacy paradigm. Our proposed method guarantees robust privacy protection while preservi…

Conformal Link Prediction with False Discovery Rate Control
Wenqin Du, Wanteng Ma, Dong Xia, Yuan Zhang, Wen Zhou
https://arxiv.org/abs/2507.07025 https://…

Conformal Link Prediction with False Discovery Rate Control
We propose a new method for predicting multiple missing links in partially observed networks while controlling the false discovery rate (FDR), a largely unresolved challenge in network analysis. The main difficulty lies in handling complex dependencies and unknown, heterogeneous missing patterns. We introduce conformal link prediction ({\tt clp}), a distribution-free procedure grounded in the exchangeability structure of weighted graphon models. Our approach constructs conformal p-values via a …