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 12
The Efficient Frontier from a LASSO Solver
In a recent paper, Schmelzer and Hastie argue that Markowitz's Critical Line Algorithm and the LASSO path trace the same curve.SPARK: A General Goodness-of-Fit Assessment via Residual Projection
Goodness-of-fit testing is a basic tool for assessing whether a fitted procedure has captured the systematic information contained in the covariates.When Noise Estimation Hides Basis Misspecification in Repeated Bayesian Inverse Problems
Basis-restricted priors in Bayesian inverse problems can lose coverage when the truth has components outside the basis.Latent Inference-Time Guidance of Time Series Foundation Models
Time Series Foundation Models (TSFMs) currently provide state-of-the-art results in forecasting tasks.Generative sequence modeling for infinite memory processes via predictive states
We consider estimating the one-step-ahead conditional distribution of a multivariate stochastic process.Adaptive mixture variational inference for spike-and-slab regression
Correlated predictors can support competing sparse explanations with similar predictions, making joint uncertainty about variable inclusion difficult to capture with mean-field approximations.Bandits with Multiple Optimal Arms: Minimax Regret and Non-Adaptivity
We study multi-armed bandits (MAB) with multiple optimal arms, motivated by the fact that many practical decision making problems admit multiple correct answers.Forecast-Necessary Causal Discovery for Nonlinear Political Panel Data: Feedback, Functional Form, and the Dynamics of Democratization
A non-significant coefficient in a dynamic panel model need not imply the absence of a relationship.Price Stability in the European Union: A Systemic Approach Using Random Matrix Theory
Price stability remains a pillar in monetary policy practices and carries a special importance within monetary unions.Isotonic surrogate modeling for computer experiments with many input variables
Virtual simulators are widely used for studying complex physical phenomena, from particle collisions to rocket propulsion.Guided Uncertainty-Aware Robust Domain Transfer
Artificial intelligence models deployed as clinical decision support tools often suffer substantial performance degradation over time and across institutions due to covariate shift, concept drift…Graph-Split Bayesian Causal Forest for Spatial Heterogeneous Treatment Effect Estimation
In spatial observational studies, treatment assignment and outcomes often exhibit spatial dependence patterns, and treatment effects may vary across space and subpopulations due to both measured and…Copula Active Subspaces I: A Score-Covariance Method for Reduced-Order Non-Gaussian Density Estimation
In Bayesian inference problems with non-Gaussian observation noise, the posterior is only as accurate as the noise density, and gradient-based samplers need that density and its gradient evaluable…Finite-Sample Theory for Fitted Q-Iteration When Actions Are Functions
Offline reinforcement learning seeks optimal decision rules from previously collected data.LOCO-AdaMP: Built-in LOCO Inference for Adaptive Minipatch Ensembles with Enhanced Prediction
As black-box machine learning models become increasingly common, extracting interpretations with uncertainty quantification has become a critical challenge.Can Tabular Foundation Models Amortize Statistical Inference?
For decades, statistical inference has largely been developed one problem at a time.Which Self-Improvements Should We Trust? Reliable Self-Improvement When Agents Reuse Their Benchmarks
As recursive self-improvement (RSI) rapidly advances, reliable evaluation becomes critical for guiding adaptive search.Sharp training-conditional coverage for conformal prediction under covariate shift
Weighted split conformal prediction reweights calibration scores by the likelihood ratio between the test and training covariate distributions and guarantees marginal coverage under covariate shift.How Synthetic Labels Improve Conformal Prediction: A Perspective on Conditional Coverage
Conformal prediction provides distribution-free finite-sample marginal coverage, but post-hoc calibration data may be too scarce to learn how uncertainty varies across inputs.ViBR-WM: Visual Bayesian Regression for World Modeling
Modeling temporal dependence and uncertainty is central to forecasting with world models.