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 6
Censored Flow Matching for Synthetic Time-to-Event Generation
Generating time-to-event data is increasingly important in survival analysis, but it remains statistically challenging because event times are often censored and thus prevent flexible generative…Pathwise Shrinkage and Probabilistic Calibration of the Multiplicative Half-Cauchy Process
We characterize the multiplicative half-Cauchy process through its log-scale random-walk representation, establishing shrinkage, expansion, and critical regimes, almost-sure summability, local…Online Quantile Omniprediction for Proper Losses
The task of constructing a predictor that is simultaneously accurate under multiple evaluation metrics has been popularized recently under the name omniprediction.Controlling the False Discovery Rate via Model-Agnostic Importance Measures
Selecting covariates while controlling the false discovery rate (FDR) is critical for interpretable scientific discovery, especially with complex machine learning models.Extending the Oracle Approximating Shrinkage to General Distributions
The Oracle Approximating Shrinkage (OAS) estimator provides a closed-form, data-driven shrinkage intensity for regularizing sample covariance matrices toward a scaled identity target, but its…Source-Aware Dynamic Pricing with Strategic Buyers
In modern commerce, firms and platforms typically use customer features from different sources to personalize prices: some features are verified using seller records or third-party data, while…Subdata Selection for Measurement-Constrained Linear Regression under Model Misspecification
In many modern applications, obtaining response variables is costly, time-consuming, or technically challenging, making it impractical to measure outcomes for all available units.Common Signals, Spurious Cross-Excitation, and Identification in Multivariate Hawkes Processes
Multivariate Hawkes models are widely used to measure endogenous interactions in event data, but latent common signals can generate apparent excitation even without structural interaction.Maximum likelihood, neural networks and posterior mean for inferring fractional Brownian motion parameters
Fractional Brownian motion (fBM) is a model of anomalous diffusion parameterised by a Hurst exponent $H \in (0,1)$ and a generalised diffusion coefficient $D_H > 0$.Hierarchical Gaussian Processes for Survey Weighting: Local Equivalent Weights in Structured Populations
Regression-based survey weighting connects model-based population estimation with design-based inference through equivalent weights.Testing Conditional Distribution Equality Using Generative Models
Conditional distribution equality testing is a core problem with applications in causal inference, model diagnostics, and transfer learning.A Mixture-of-Experts Framework for Multi-Stream Quickest Change Detection
A Mixture-of-Experts (MoE) framework is proposed for quickest change detection (QCD) where there are multiple experts, and each expert produces a sequence of statistics for change detection.Flexible Doubly Robust Proximal Causal Inference for Censored Outcomes
Estimating causal effects on right-censored time-to-event outcomes is challenging under unmeasured confounding.textGrain watermarking is harder to detect: can sum-tests still do it optimally?
Watermarking embeds an imperceptible signal in text from large language models that is statistically detectable with a secret key.E-Backtesting Expected Shortfall without Value-at-Risk Forecasts under Time-series Models
Expected Shortfall (ES) measures the average size of tail losses.Linear Regression Beyond Covariance: Latent Slope Spectra from Higher-Order Cumulants
Least squares summarizes the joint distribution of a response and regressor through second-order information.ndscape: a structural map of the nested dichotomy space
Nested dichotomies (NDs) reduce multiclass classification to a tree of binary classifiers.Strategic Multi-Agent Resource Allocation via Statistical Contracts
We study a class of strategic hypothesis testing problems in which a principal aims to allocate resources among competing agents who possess data or data-generating mechanisms that may be relevant…Risk Measures for the Climate Transition Age: Why Symmetric Risk Budgets are unlikely to mitigate two-dimensionally asymmetric Climate Change risks
The most widely used investment risk measures - variance, standard deviation, and tracking error - treat upside deviations as equally undesirable as downside losses.How Low Can You Go: Fine-Tuning LLMs with Bayesian Optimization (LlMBO)
Recently, Morris et al. introduced TinyLoRA, a strategy for LLM fine-tuning which introduces arbitrarily few parameters, and demonstrated accuracy improvements using less than $10$ parameters rather…