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
Changepoint localization via nested exchangeability tests: from point estimates to confidence intervals
We study distribution-free uncertainty quantification for a changepoint estimate in an offline setting.Statistical and Privacy Guarantees for Deep M-Regression via Stochastic Gradient Langevin Dynamics
In this article, we study deep nonparametric regression trained via stochastic gradient Langevin dynamics (SGLD), a variant of stochastic gradient descent that injects carefully calibrated Gaussian…Adaptive Experimentation Can Hide Model Error:Detection Limits and Falsification Budget
An experiment can be informative about the parameters of a working model and uninformative about whether that model is wrong.Identification and Bounding of Joint Expectation over Potential Outcomes
The joint expectation of functions of potential outcomes $(Y_1,Y_0)$ is a fundamental quantity in causal inference, particularly when investigating the variation or heterogeneity of causal effects.Joint Modeling of Conditional Mean and Variability in Multilevel Functional Data: Application to Postprandial Glucose Trajectories in the AEGIS Study
Functional data analysis provides a framework for studying time-varying associations between covariates and high-frequency biological signals.Doubly-robust causal inference for multi-valued treatment regimes with recurrent neural networks
We propose a doubly-robust method to estimate the causal effects of multi-valued treatment regimes on patient outcomes.Between Hi-Fi and MMI: the overlap of redundancy and synergy in partial information decomposition
Partial information decomposition (PID) splits the information that two sources carry about a target into redundant, unique and synergistic parts, with nonnegative decompositions lying on a segment…Prior-data fitted networks for variable selection
Prior-data fitted networks (PFNs) have become state-of-the-art for tabular prediction but remain underexplored in other areas of statistics.Nonparametric GARCH: An Iterated Neural Estimator
We study nonparametric estimation and forecasting for GARCH-type processes and propose an iterated neural estimator that alternates between estimating the recursion map and updating the unobserved…Aggregate Calibration Can Mask Stress-State Undercoverage: Bounded-Memory Forecast Intervals under Recurrent Financial Stress
Sequential forecast intervals are often evaluated by aggregate coverage even when calibration memory is bounded.Directional Statistics in Signal Processing: A Geometry Driven Overview of Models, Learning, and Applications
Many signal-processing objects are defined by periodicity, normalization, sign or global-phase ambiguity, basis invariance, or positive-semidefinite constraints and therefore cannot be modeled…Amortized Nonlinear Variable Selection with Prior-Data Fitted Networks
Variable selection is an important task for interpreting potentially complex systems in fields such as biology, business, and sociology.Post-Inference Model Chaining: A Problem Statement and a Model-based Bootstrap for Training End-to-End Digital Twins
End-to-end digital twins can be constructed by fitting separate stage models and connecting them after inference.Post-stratified importance sampling
Importance sampling is a building block of modern statistical computing, but the weighted estimate is prone to high or infinite variance.Balance-Guided Forest for Causal Effect Estimation
Covariate balancing is widely used for estimating causal effects in observational studies.A Scientific Claim Stability Framework for Evaluating MRI Morphometry Pipelines
MRI morphometry pipelines are often evaluated by measurement agreement or downstream accuracy, yet their scientific value depends on the conclusions they support.Diagnosing Model--Item Interactions in Language Model Benchmarks with LSIRM
Language models with similar benchmark accuracy can succeed on different items.Where Is a Prediction Model Miscalibrated, and for Whom? Local Audits in Outcome-Learned Pockets
Overall and group-level averages, and ranking metrics such as the AUC, can hide sets of observations for which a prediction model systematically over- or under-predicts.Causal discovery needs explicit epistemic standards
There are many causal discovery algorithms, but few guiding principles for choosing between them.Bagel: A Fast Bayesian Online Changepoint Detection Algorithm for Linear Models via Posterior Merging
Bayesian online changepoint detection provides a natural framework for quantifying uncertainty about whether and when a change has occurred.