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 10
Errors of LLM-Assisted Literature Retrieval in Environmental Science: A Comparison Study of Abstract versus Full-text Based Prompts
Large language models (LLMs) are increasingly used for literature search and synthesis.A Dirichlet Mixed-Membership Model for Exact Multivariate Distributional Credibility
Credibility theory combines individual experience with portfolio information for insurance pricing, but classical formulations focus primarily on conditional means and expected premiums.Finite-Sample Distribution Theory and Efficient Large-Scale Inference for Online Quantile Regression
This paper studies online quantile regression for large-scale and streaming data using Stochastic SubGradient Descent (SSGD) with constant learning rates.DAG-CLIP: A DAG Learning Framework in the Presence of Latent Variables
Learning directed acyclic graphs (DAGs) to uncover causal mechanisms has attracted substantial attention in machine learning.Valid Stopping in Adaptive Generator-Verifier Loops
Numerous agentic workflows are based on a generator-verifier loop: a generator proposes candidates, a cheap verifier scores them, and the workflow terminates when a proposal is verified as good…Inverse Cross-spectral Neural Networks for Multivariate Time Series
CoVariance Neural Networks and their extensions have emerged as effective tools for processing multivariate data, deriving graph shift operators directly from second-order statistics.Multi-Task Active Learning with Efficient Resource Allocation
Many scientific studies allow costly auxiliary information to be collected during data labeling but not at deployment.Data Fusion for Errors-in-Variables
We study errors-in-variables problems in which a target study contains only a single error-prone surrogate of an unobserved exposure, while an external source study provides repeated surrogate…Poisson empirical Bayes estimation of sums of random variables via minimum-distance methods
The estimation of sums of functions of observable and unobservable variables is a long-standing problem in statistics, with applications in many domains.Empirical-Bayes spectral partial pooling across related tasks
Spectral methods are central to high-dimensional statistics and machine learning, underlying procedures for covariance estimation, matrix denoising, representation learning, clustering, and latent…Assumption-lean logistic regression with missing covariates
Missing covariates are frequently encountered in supervised learning problems, and classical methods for estimation using such data use carefully chosen imputation schemes for missing data, or…HyperNSDE: Personalized Neural SDEs for Joint Static-Longitudinal Clinical Data Generation
Synthetic patient data generation is a promising solution to the dual challenge of data scarcity and privacy constraints in healthcare machine learning.A perspective note on likelihood approximation and inference for complex simulation models using a chain of aggregated normalizing flows
We present a new perspective on the problem of likelihood approximation within the framework of simulation-based inference that promotes scalable and controllable simulation routines for large-scale…Active Feature Acquisition for Cost-Efficient Temporal Prediction with Reduced Participant Burden
Accurate forecasting of pathological outcomes is a central problem in psychology.Same Predictions, Different Harms: Causal Auditing of Patient World Models
Patient world models used for clinical trial simulation can agree on transition kernels and arm-specific risks, yet disagree on the fraction of patients harmed by switching treatment---the…Causal Lag Structure Discovery in Confounded Time Series via Orthogonalized Adaptive Estimation
Finding which variables cause which others in multivariate time series, and at what lags, is central to science and policy, yet existing methods force a choice between flexible confounder…Neyman-Pearson Classification with Indecisions: Simultaneous Control of Selective Type I and Type II Errors
Binary classification often requires controlling both type I and type II error rates at prespecified levels.Calibration Assessment for Nested Uncertainty Sets
We present a Bayesian framework for assessing the calibration of nested uncertainty sets from simulation studies.Jumping up and down: Denoiser diffusion models for discrete ordinal data
Diffusion models are highly developed in continuous spaces for image and video domains.A Residual Tree Gaussian Process Modeling Framework for High-Dimensional Data
With the advance of measurement technologies and increasing computing power, large spatial data with heterogeneous structures are often collected over high-dimensional domains.