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.
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Newest papers, page 16
On the Identifiability of Mixed Ordinal and Exponential Family Causal DAGs under Linear Parametric Models
The problem of identifiability in linear parametric models (LPMs) whose nodes follow either an ordered logit model or a regular one-parameter exponential family is evaluated.When a High Score Is an Illusion: Certifying Genuine versus Repackaged Forecasting Skill
Ranks depend on the observations used for comparison.Causal Path Analysis from Perturbational and Population-Scale Single-Cell Data with Multiscale Confounding and Measurement Error
Single-cell perturbation experiments provide causal information on gene regulation, whereas population-scale single-cell studies characterize gene expression and phenotypes in human populations.Supervising the Chain Ladder
The chain ladder's volume-weighted pattern minimises an explicit loss function, yet is rarely booked as such.Equitable Partition Realizability for Dynamics-preserving and Privacy-aware Network Reconstruction
Degree-sequence realizability is the combinatorial basis of configuration models, but degree constraints alone do not ensure the preservation of graph dynamics.Causal Discovery via Transformed Low-Rank Quantile Surfaces
We propose Low-Rank Quantile Surfaces (LRQS), a bivariate causal model in which, in the causal direction, an unknown monotone transformation of the conditional quantile surface admits a low-rank…Covariate Selection for Doubly Robust Double/debiased Machine Learning Estimators for Causal Inference
High-dimensional data create challenges for causal effect estimation because identifying the covariates needed for correct model specification becomes increasingly difficult.Conformal Policy Learning with Distribution-Free Safety Guarantees
Policy learning aims to determine who should be treated based on individual characteristics.When AI Generates Covariates: Causal Typing and Estimand Drift in Sequential Experiments
AI-generated covariates from notes, conversations, images, and wearable streams can change the causal question when their roles are left unspecified.Information Set Emulation: Causal Certificates for AI Derived EHR Features
AI and large language models can recover clinically meaningful features from electronic health records (EHRs), but predictive usefulness does not establish admissibility for causal inference.Closed-form Bayesian homography estimation from noisy point correspondences
While homographies are fundamental to many computer vision tasks, the majority of conventional estimation techniques provide only point estimates without directly quantifying uncertainty introduced…On Detecting Multiple Simultaneous Change-points in High Dimensional Non-Stationary Time Series
This paper studies the detection of multiple simultaneous (systematic) change points for high-dimensional nonstantionary economic and financial time series data.Multi-source conformal prediction: leveraging heterogeneity via localization
Many modern prediction tasks involve data from multiple heterogeneous sources, while the test distribution may differ substantially from any individual source.A Functional SVD Framework for Regularized Multivariate Functional PCA with Dual Penalization
This paper introduces a novel framework for Regularized Multivariate Functional Principal Component Analysis (ReMFPCA) via Functional Singular Value Decomposition (SVD).Online Bayesian Node Classification on Inductive Graphs under Distribution Shift
On evolving graphs, node classifiers must satisfy two key requirements: inductive generalization to newly arriving nodes under distribution shift and calibrated uncertainty for safety-sensitive…Just add noise: Debiasing tree-based variable importance in mixed data
Variable importance scores from tree-based methods such as random forests favor continuous predictors over categorical ones.On Large-Scale Multiple Testing Over Networks: A Non-Asymptotic Approach
Distributed multiple testing asks $N$ sites to control a global false discovery rate (FDR) under a tight communication budget.PDE-constrained inverse problems at the $\sqrt{n}$ rate via debiased physics-informed neural networks
We study the problem of estimating unknown parameters in PDE-constrained inverse problems from noisy observations, where the PDE solution is approximated using Physics-Informed Neural Networks…A Ranking Approach for Measuring Calibration
When providing forecasted probabilities with a predictive model, the ideal model offers perfect calibration: the true probability of the outcome (i.e., the probability that $Y=1$) exactly matches…Synthetic Nearest Neighbors: Extending Synthetic Controls for Matrix Completion with Missing Not at Random Data
We develop a causal framework for matrix completion under missing not at random (MNAR) data.