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 9
Finite-Rank Logistic Gaussian Processes with Exact Likelihood for Conditional Density Estimation
Conditional density estimation describes how the entire distribution of a response changes with covariates, and in imaging studies also with location.Cluster-Robust Prediction-Powered Inference
Data collection is often costly or logistically demanding, limiting both the questions researchers can pursue and how precisely they can answer them.Extreme Binary Classification: Extreme Value Theory for Extreme Constraint on False Negative
While binary classification is one of the most extensively studied problems in machine learning, the regime in which the goal is to learn a classifier with an almost zero false negative rate remains…Policy Learning with Weak Signals
Policy learning in digital experimentation faces three challenges: weak signal-to-noise ratios, rich covariate spaces, and massive data volumes.Conformal Prediction for Spatially Dependent Data via Sequential Whitening
Split conformal prediction uses prediction errors on held-out (calibration) data to determine how wide the prediction intervals should be.Estimating Uncoded Crash Factors with Tabular Foundation and System One Models: Kumo Tabular and Jev
Road safety programs count the coded fields of police crash records, while the officer's narrative, which often records factors the fields omit, is rarely read.D-SLR: The Disjoint Row-Sparse plus Low-Rank Decomposition
Compressing a matrix for reconstruction still defaults to the truncated SVD, approximating the data with a single low-rank structure.Transformed Samplers with Variance Reduction
Markov chain Monte Carlo (MCMC) methods are the standard tool for computing expectations under complex probability distributions.Trustworthy Method Comparison with AI Judges: Estimation and Design under Order, Batch, and Aggregation Effects
Large language models (LLMs) are increasingly used as judges for automated AI evaluation.A Functional Representation of Credit Behavior for Probability of Default Modeling
This paper proposes a framework for modeling probability of default via functional data analysis.Beyond Marginal Monitoring: Distributed Joint-Distribution Testing for Data Concept Drift in Large Scale E-Commerce Operations
Concept drift threatens production machine learning, yet the empirical behavior of multivariate two-sample drift detectors at scale remains under-characterized.Anytime-valid simulation-based hypothesis testing
For a given data distribution $(X_t)_{t \in \mathbb{N}} \sim Q$ i.i.d., we investigate the hypothesis testing problem: $H_0: Q = P_0$ vs. $H_1: Q = P_1$, for two different model probability…Where Do Two Populations of Persistence Diagrams Differ? Calibrated Local Inference at a Fixed Budget
Many two-sample tests for populations of persistence diagrams assess global differences without identifying the regions of the birth-death plane that contribute to them.High-Dimensional Statistical Inference for Sparse Support Vector Machines
Using a replica-symmetric high-dimensional characterization, we develop an inferential framework for sparse support vector machines when the sample size and number of features grow proportionally.When does conformal calibration need censoring weights? Cause-of-failure prediction sets under competing risks
Split conformal prediction sets for competing-risks labels at a fixed horizon require calibration labels that right censoring can leave unobserved.Prediction-powered inference for time series across space
The following motif is common in spatiotemporal settings: we have a sequence of covariate and label pairs observed for a relatively short, recent time period.Covariate-dependent Joint Modeling of Multivariate Ordinal Preferences and Its Connections with Comparison Models
Multivariate ordinal data along with covariates are commonly collected in problems ranging from alignment of language models with human preferences, as well as in recommender systems.Relative Wasserstein Spatial Depth for Cluster Number Selection in Distributional Data
Contemporary data in many scientific domains, such as images, media streams, biomedical omics, are naturally modeled as probability distributions in Wasserstein space instead of points in Euclidean…Sketched Calibration for Conformal Prediction under Covariate Shift
Weighted conformal prediction corrects for covariate shift by reweighting calibration scores with the likelihood ratio between target and source covariates.Certified Corruption Budgets: Anytime-Valid Leaderboard Claims under Adaptive Rigging
Public leaderboards for AI models are read continuously, and attackers can see every published standing.