Probability & Statistics · Mathematics & Statistics
Statistics Theory
The newest papers in Statistics Theory that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
321 papers, from 31 August 2026 to 4 November 2026: 287 arXiv preprints and 34 OpenAlex records.
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Poisson Autoregression on a Large Network with a Stochastic Block Model Structure
We consider multivariate Poisson autoregressive processes evolving on random networks generated by a Stochastic Block Model.Efficient Mass Matrix Estimation with Gaussian Cooling
We introduce an algorithm for efficiently preconditioning log-concave and log-smooth distributions that scales logarithmically with the condition number of the underlying distribution.On some concepts of convergence and their connections
The notions of classical two-scale convergence and the periodic unfolding method, which both rely on an additional scale for the representation of periodic micro-oscillations, are in this paper…Generalization Bounds for Flow-matching Generative Models for Intrinsically Low-dimensional Data
Despite the remarkable empirical success of flow-matching models, their statistical generalization guarantees remain underdeveloped.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.Cross-Fitting Under Nonregularity: Normality and Inference via Locality
Cross-fitting is routine in much of applied research.Gradient-Guided Density Peak Clustering
Density peak clustering (DPC) connects each observation to its nearest neighbor of higher density and identifies cluster centers as high-density observations with unusually large nearest neighbor…Polylogarithmic Sparsity of Randomly Reweighted NPMLEs for Gaussian Mixtures
The nonparametric maximum likelihood estimator (NPMLE) of a Gaussian location mixture maximizes the likelihood over the infinite-dimensional space of mixing distributions.Counterfactual Generation via Flow Matching: Coupling-Sensitive End-to-End Rates
Counterfactual generation seeks to sample outcomes under a hypothetical intervention or decision using observational data collected under the factual assignment mechanism.Feature Selective Model Collapse in Diffusion Models: Total Replacement versus Fixed-Budget Training
Model collapse arises when generative models are trained on synthetic data produced by earlier models.Sample complexity bounds for categorical Markov random fields via Discrete Diffusions
Many applications in statistics, economics, and physics require sampling from high-dimensional categorical distributions with local dependence structures.Expected Utility Regret Rule: Minimax and Bayes Optimal Portfolio Choice
This study considers the problem of portfolio choice, where we recommend a portfolio to an investor to maximize the expected utility of their wealth.High-accuracy simulation of Picard HMC, part I: Gaussian cloud correction
We study the problem of sampling from a continuous density $π\propto \exp(-V)$ on $\mathbb R^d$, where $V\in C^2(\mathbb R^d)$ has a $β$-Lipschitz gradient and $π$ satisfies a logarithmic Sobolev…A Dynamical Theory of LoRA in Continual Learning
Despite the widespread use of Low-Rank Adaptation (LoRA), little is known about its dynamics in continual learning and the mechanisms by which low-rank updates affect catastrophic forgetting.WEIRDO: WEak resIdual Regularized DOob's h-transform diffusion alignment
We study the problem of estimating the guidance that steers the distribution learned by a diffusion generative model toward a tilted target $q_0 \propto w\,p_0$ at inference time.Partial identification with entropy regularized optimal transport
In many statistical settings, the available data and maintained assumptions do not suffice to uniquely identify the model parameters of interest.Cheap to Draw, Expensive to Trust: Certifying Test-Time Scaling Curves
Sampling several answers and keeping the one a verifier scores highest is one of the simplest ways to buy accuracy at test time.Learning Local Fermionic Lindbladians under Parity Superselection
Parity superselection forbids direct measurement of odd Majorana observables.Optimal detection of general moment changes: Simultaneous mean and covariance change detection and beyond
We study multiple change-point detection in multivariate time series whose distributions change in a piecewise constant manner.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.