Probability & Statistics · Mathematics & Statistics
Methodology
The newest papers in Methodology that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
353 papers, from 4 September 2026 to 5 November 2026: 353 arXiv preprints.
Get the few papers worth your time in Methodology every morning.
Follow Methodology and ReadSearch reads its new papers each day, then brings you the few worth reading, each with its key finding in a sentence.
Newest papers, page 7
From Fourier Amplitudes to Persistence Images for Impact-Echo Defect Detection
Impact-echo testing is a nondestructive testing method used to detect internal defects in concrete structures.Designing for Prediction: Planning Formulas for Expected Out-of-Sample $R^2$ and $r$
Design calculations let researchers gauge, before data is collected, whether a planned study is likely to detect an effect.Linear regression with probabilistic networks for network-linked data
Network-linked data combine a network with covariates and responses observed for its nodes.Theoretical and Practical Advances in Hierarchical Bayesian Deep Learning Methodology
Reliable uncertainty quantification remains an important challenge in AI.Neural Supervised Clustering for Learning Individualized Treatment Rules with Many Treatments
Learning an individualized treatment rule (ITR) with many treatments requires estimating treatment-specific effects from limited observations per arm.Student Copula Markov Networks
This paper extends the applicability of probabilistic graphical models to non Gaussian data.Generalizing Identifiability Results in Causal Discovery from Complete Data to Missing Data Settings
While causal discovery methods have matured for complete data, such algorithms for handling missing values remain limited.Multi-Sensitive Distributional Fairness: Joint Wasserstein Audits and Shapley Allocation
Fairness audits with several sensitive attributes are often performed one coordinate at a time.Distributional Regression for Arbitrary Response Variables Using ReLU Neural Networks
This paper develops a unified framework for distributional regression with arbitrary response variables.When Sample-Splitting Choices Matter in TMLE and AIPW: Cross-Fitting, Targeting, and Repetition
Variants of targeted maximum likelihood estimation (TMLE) and augmented inverse-probability weighting (AIPW) with machine-learning nuisance models differ in which observations train them, how bias…Online Individualized Treatment Selection under Partial Feedback: False Discovery Control and Minimax Optimality
We study online individualized treatment selection when only the outcome under the assigned treatment is observed.Locally adaptive step sizes for Hamiltonian Monte Carlo methods
Exact reversible locally adaptive step sizes are introduced by Bou-Rabee et al. (2026) in the context of the No-U-Turn Hamiltonian Monte Carlo (HMC) sampler for the purpose of high performance HMC…Quickest Change-point Detection in Generalized Linear Models with Variational Estimation
We consider quickest change-point detection in generalized linear models when the post-change parameter is unknown.Measuring Variable Importance Using Proportional Inclusive Marginal Effects
Shapley-based measures are widely used for measuring variable importance in statistics and machine learning.Implicit and explicit reparameterizations of the Folded Normal and Rice distributions
Variational inference is a flexible tool for modeling experimental data and has proven useful in crystallography.Selective Inference for Clustering Under Unknown Covariance
Unsupervised clustering is widely used to discover underlying group structure, after which researchers often seek to validate these groups via post-hoc hypothesis testing on the population means.Deep Neural Mixed Models for Nonlinear Longitudinal Prediction with Subject-Specific Heterogeneity
Longitudinal health data often exhibit within-subject correlation, subject-specific heterogeneity, and complex nonlinear predictor--response relationships that may not be adequately captured by…Adaptive Robust Confidence Sets in High Dimensions
We study adaptive confidence sets for mean vectors of multivariate Gaussians with identity covariance under Huber contamination when the contamination proportion is unknown.Small area learning
Small area estimation (SAE) traditionally borrows strength across domains through a prespecified working model.Prior-Informed Generalizable Low-Rank Representation Learning via Regularized Adversarial Reweighting
Modern learning problems increasingly leverage data from heterogeneous sources to improve robustness and generalizability under distribution shift, often with high-dimensional parameters admitting…