Ariel Linden: A Goodness-of-Fit Test for Mixed-Effects Logistic Regression https://arxiv.org/abs/2604.19694 https://arxiv.org/pdf/2604.19694 https://arxiv.org/html/2604.19694

Yuanchuan Guo, Buyu Lin, Jun S. Liu: PRADAS: PRior-Assisted DAta Splitting for False Discovery Rate Control https://arxiv.org/abs/2604.19517 https://arxiv.org/pdf/2604.19517 https://arxiv.org/html/2604.19517

Mehmet S{\i}dd{\i}k \c{C}ad{\i}rc{\i}, Yener \"Unal: A Nonparametric Goodness-of-Fit Test for High-Dimensional Generalized Gaussian Distributions via Nearest-Neighbor Graphs https://arxiv.org/abs/2604.19493 https://arxiv.org/pdf/2604.19493 https://arxiv.org/html/2604.19493

Simon Pauli, Andreas Futschik: Random Reward Phase-Type Distributions with Applications in Latent Severity Modeling https://arxiv.org/abs/2604.19378 https://arxiv.org/pdf/2604.19378 https://arxiv.org/html/2604.19378

Camacho, Ezenarro, Schorn-Garc\'ia, Westerhuis: From design of experiments to analysis of variance of multivariate data: a tutorial review on ANOVA simultaneous component analysis https://arxiv.org/abs/2604.19265 https://arxiv.org/pdf/2604.19265 https://arxiv.org/html/2604.19265

Gyeonghun Kang, Jialiang Mao, Li Ma: Multiscale Cochran-Mantel-Haenszel Scanning for Conditional Dependency https://arxiv.org/abs/2604.19177 https://arxiv.org/pdf/2604.19177 https://arxiv.org/html/2604.19177

Pranoy Palit, Ayan Pal, Kiran Prajapat: A Finite Mixture Failure-rate based Heterogeneous Step-stress Accelerated Life Testing (h-SSALT) Model https://arxiv.org/abs/2604.19169 https://arxiv.org/pdf/2604.19169 https://arxiv.org/html/2604.19169

Yong He, Kangxiang Qin, Haoran Tang: Transfer Learning for Degree-Corrected Mixed Membership Network Models https://arxiv.org/abs/2604.19152 https://arxiv.org/pdf/2604.19152 https://arxiv.org/html/2604.19152

Cristiano Villa: The General Formulation of Loss-Based Priors for Parameter Spaces https://arxiv.org/abs/2604.19150 https://arxiv.org/pdf/2604.19150 https://arxiv.org/html/2604.19150

Awan Afiaz, M. Shafiqur Rahman: Overstuffed sandwiches and separation anxiety: finite-sample variance estimation for penalized GEE with near-separated binary data https://arxiv.org/abs/2604.18863 https://arxiv.org/pdf/2604.18863 https://arxiv.org/html/2604.18863

Nils Lid Hjort, M. C. Jones: Locally parametric nonparametric density estimation https://arxiv.org/abs/2604.18657 https://arxiv.org/pdf/2604.18657 https://arxiv.org/html/2604.18657

Shengjun Wu, Jeffery Wu: How to quantify direct correlations between variables https://arxiv.org/abs/2604.18653 https://arxiv.org/pdf/2604.18653 https://arxiv.org/html/2604.18653

Ibrahim Halil Tanboga: Stable Transport Meta-Analysis for Heterogeneous Cardiovascular Trials: A Nuisance-Anchor Framework with a Sign-Stability Diagnostic https://arxiv.org/abs/2604.18646 https://arxiv.org/pdf/2604.18646 https://arxiv.org/html/2604.18646

[2026-04-22 Wed (UTC), 13 new articles found for statME Methodology]

Ping Zeng, Yicheng Zeng, Lixing Zhu: Missingness-Adaptive Factor Identification in High-Dimensional Data https://arxiv.org/abs/2604.18497 https://arxiv.org/pdf/2604.18497 https://arxiv.org/html/2604.18497

Carlos Garc\'ia Meixide, David R\'ios Insua: Shrinkage through multiple identifiability https://arxiv.org/abs/2604.18430 https://arxiv.org/pdf/2604.18430 https://arxiv.org/html/2604.18430

Mei Dong, Linbo Wang, Lin Liu, Oliver Dukes: Order Dependence in Regression by Composition: Discussion on "Regression by Composition'' by Farewell, Daniel, Stensrud, and Huitfeldt https://arxiv.org/abs/2604.18388 https://arxiv.org/pdf/2604.18388 https://arxiv.org/html/2604.18388

Wolfgang Messner: Effect Sizes in Marketing Research: Why Cohen's Local f^2 Belongs in the Toolkit https://arxiv.org/abs/2604.18363 https://arxiv.org/pdf/2604.18363 https://arxiv.org/html/2604.18363

Xi Fang, Guangyu Tong, Yuan Huang, F. Perry Wilson, Patrick J. Heagerty, Fan Li: Statistical inference with win statistics in cluster-randomized trials with composite outcomes https://arxiv.org/abs/2604.18341 https://arxiv.org/pdf/2604.18341 https://arxiv.org/html/2604.18341

Yongdong Ouyang, Monica Taljaard, James P. Hughes, Fan Li: Which Small-Sample Correction Should Be Used When Analyzing Stepped-Wedge Designs with Time-Varying Treatment Effects? https://arxiv.org/abs/2604.18323 https://arxiv.org/pdf/2604.18323 https://arxiv.org/html/2604.18323

Mark Louie F. Ramos, Ph. D: Embarrassingly Causal: Causal Use of Associational Data in Magic The Gathering Drafts https://arxiv.org/abs/2604.18314 https://arxiv.org/pdf/2604.18314 https://arxiv.org/html/2604.18314

Ulysse Naepels, Victor M. Panaretos: Inference for Functional Data under Markov Constraints https://arxiv.org/abs/2604.18229 https://arxiv.org/pdf/2604.18229 https://arxiv.org/html/2604.18229

Denis Rustand, H{\aa}vard Rue, Lisa Le Gall, Karen Leffondre: Efficient Bayesian inference for non-linear association structures in joint models: A hierarchical approach via INLA https://arxiv.org/abs/2604.18057 https://arxiv.org/pdf/2604.18057 https://arxiv.org/html/2604.18057

Hossein Mohammadi: An ensemble-based approach for multi-fidelity emulation and adaptive sampling https://arxiv.org/abs/2604.18045 https://arxiv.org/pdf/2604.18045 https://arxiv.org/html/2604.18045

Roland B. Sogan, Tabea Rebafka, Fanny Villers: A Bayesian framework with adaptive elastic nets for the inference of Gaussian graphical models https://arxiv.org/abs/2604.18042 https://arxiv.org/pdf/2604.18042 https://arxiv.org/html/2604.18042

Nicholas Williams, Alejandro Schuler: Improving reproducibility by controlling random seed stability in machine learning based estimation via bagging https://arxiv.org/abs/2604.17694 https://arxiv.org/pdf/2604.17694 https://arxiv.org/html/2604.17694

Yukai Yang, Rickard Sandberg: Subsample-based Estimation under Dynamic Contamination https://arxiv.org/abs/2604.17676 https://arxiv.org/pdf/2604.17676 https://arxiv.org/html/2604.17676

Chad M. Topaz: A Null Model for Mapper Subtype Claims https://arxiv.org/abs/2604.17395 https://arxiv.org/pdf/2604.17395 https://arxiv.org/html/2604.17395

Baichen Yu, Xuetong Li, Jing Zhou, Hansheng Wang: Detecting Breast Carcinoma Metastasis on Whole-Slide Images by Partially Subsampled Multiple Instance Learning https://arxiv.org/abs/2604.17254 https://arxiv.org/pdf/2604.17254 https://arxiv.org/html/2604.17254

Mateen R Shaikh: Model Selection and Parameter Inference through Constraints via Sequences of Surrogate Smoothing Functions https://arxiv.org/abs/2604.17154 https://arxiv.org/pdf/2604.17154 https://arxiv.org/html/2604.17154