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 15
The Informational Content in Lepto-Variance and Its Relation to Higher Moments
Lepto-regression is defined as the machine learning process of constructing a Regression Tree of a target feature on itself.Concept Drift from a Causal Perspective
Concept drift is a common phenomenon in real-world data streams, in which changes in the data-generating distribution can degrade predictive model performance.PICPIs: Prediction-Interval-Conditional Prediction Intervals
A classical question in statistics is which observable quantities to condition on when drawing inferences about unobservable targets.Distributional Balancing with Machine Learning for Clinical Trial Augmentation Using Real-World Data
In clinical trials, randomization of treatment and control groups is typically used to ensure the groups have similar covariate distributions on average, resulting in unbiased causal effect…PACE: Plug-and-Play Contextual Embedding for Feature Screening with Pretrained Tabular Foundation Models
In high-dimensional tabular learning, feature screening provides a lightweight, model-agnostic way to remove irrelevant features before model fitting.Belted Engression: Sufficient Dimension Reduction for Generative Distributional Regression
Modern conditional generative models face significant challenges when learning complex covariate dependencies.Sparse Regression Distilled from a Single Robust Fit
Robust linear fits can resist response contamination yet remain too dense or unstable for useful global explanations.Auditing Bayesian Graph Alignment: Diagnostic Comparisons and Reference Failure
Bayesian graph alignment estimates correspondence probabilities, but convergence of an alignment-score trace need not imply accurate correspondence marginals.The Price of Self-Calibration: Exact Evidence Budgets and Manufactured Blind Sets in Adaptive Monitoring
Self-calibrating monitors adapt their threshold online to guarantee a prescribed long-run false-alarm rate under arbitrary drift.Reliability-Centered Evaluation of Sparse Longitudinal CT Lesion-Size Forecasting with Conformal Interval Calibration and Gompertz-Inspired Regularization
Sparse longitudinal CT follow-up limits lesion-size forecasting when only a few prior observations are available.Robust Dual-Regularized Variable Selection under Outlier Contamination
Real data often contain unusual observations that can exert disproportionate effects on variable selection, especially in complex predictor settings.Improving the Predictive Performance of Bootstrap Aggregating by Dirichlet Resampling
We revisit Breiman's observation that reducing inter-tree correlation without weakening individual trees can improve random forests.Riemannian Simultaneous Inference for Tangent Vector Field Regression
We consider nonparametric tangent vector field regression on a Riemannian manifold without boundary.Bridging Local and Population Causal Effects: A Proximal Instrumental Variable Approach
Instrumental variable (IV) methods address treatment endogeneity, but with non-compliance and heterogeneous treatment effects a binary instrument generally identifies the local average treatment…Compressed Active Subspaces for Scalable Bayesian Inference
Active subspace methods provide a framework for quantifying predictive uncertainty in high-dimensional models by identifying and performing inference along parameter directions that have the…Epidemiological Causal Graph Identification: Challenges, Identifiability and Algorithms
Causal discovery from observational data is fundamental to statistics and machine learning, yet determining causal direction without interventions necessitates structural assumptions.Robust Multi-Task Learning for Principal Component Analysis
Principal component analysis (PCA) is a fundamental tool for learning low-dimensional structure from high-dimensional data.Instance-Optimal Adaptive Location Estimation via Multiscale Mid-Summaries
Location estimation exhibits markedly different finite-sample behavior across noise distributions: regular families typically yield root-\(n\) rates, whereas compactly supported laws may admit…Prediction-Powered Smoothing and Validation for Disaggregated AI Evaluation
Evaluating an AI system requires disaggregated assessment, as performance varies across domains such as benchmark task types or conversation types in deployed agents.Aggregated Posterior Predictive Checks for Generative Modeling
Latent variable generative models are commonly fit using simple priors over latent variables, but draws from these priors often fail to produce realistic data.