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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Newest papers
Towards the Query Complexity of Sampling under Strong Log-Concavity and Log-Sobolev Inequalities
We study the query complexity of sampling from smooth distributions on $\mathbb R^d$ using first-order oracles.Functional inequalities from the fundamental limits of Bayesian estimation
We develop a Bayesian information-theoretic framework for proving functional inequalities for log-concave measures.Polynomial-Time Differentially Private Synthetic Graph Release with Nearly-Linear Additive Error
We give a polynomial-time $(\varepsilon, δ)$-edge-differentially private algorithm that releases a synthetic graph approximately preserving all cut values of a nonnegative weighted input graph $G$.Regularization vs. Convolution in Kernel-Based Hypothesis Testing
The maximum mean discrepancy (MMD) test is a popular nonparametric test on general domains.Fundamental Limits of Coherent Imaging in The Presence of Atmospheric Turbulence
We study the fundamental limits of jointly estimating the signal amplitude and the atmospheric phase distortion in complex-valued multi-look coherent imaging, where the measurements are corrupted by…Nonparametric GARCH: An Iterated Neural Estimator
We study nonparametric estimation and forecasting for GARCH-type processes and propose an iterated neural estimator that alternates between estimating the recursion map and updating the unobserved…A Bayesian-Geometric Reading of Classical Model-Based Optimal Design
The classical model-based optimal design criteria (D-, T-, L-, C-, and A-optimality) are usually motivated via the Cramér--Rao bound as optimizing scalar functions of the Fisher information matrix…Estimating "Realized" Skewness using Convolutional Neural Network
We propose a new estimator of low-frequency skewness (monthly) that exploits high-frequency data (5 min) through a direct functional mapping consisting of layers of Convolutional Neural Networks…Beyond Multi-Index Models: Learning Intersections of Degree-2 PTFs
We give an algorithm for PAC learning intersections of $k$ degree-2 polynomial threshold functions over $R^d$ under a Gaussian distribution.Ensembles of Random Partition-Based Models under Covariate Shift
Distribution shifts, including covariate shifts, are ubiquitous in real-world applications of machine learning.Conformal Prediction at Performative Equilibrium: The Price of Performativity
Conformal prediction gives finite-sample coverage when calibration and test points are exchangeable, but exchangeability can fail when deployment changes the data distribution.Sketching in Regression: Sketchy in Practice?
The Johnson-Lindenstrauss (JL) lemma and its extensions provide tools for solving the $L_2$ regression problem in high-dimensional settings with $n>d$, by transforming a subspace of $\mathbb{R}^{n}$…Calibrated Multiaccuracy: Deterministic and Randomized Sample Complexity
Calibrated multiaccuracy asks that a predictor be globally calibrated and multiaccurate with respect to a bounded test family $G$; that is, its residuals should be uncorrelated with every test…Specification LASSO and a Flexible Characteristics-Based Asset Pricing Model
This paper studies a partially linear semiparametric additive model that allows for interactions among variables in high-dimensional settings.Subpolynomial-Query Proximal Sampling Beyond Log-Concavity
We study the gradient query complexity of sampling from a density $π(x)\propto\mathrm{e}^{-V(x)}$ on $\mathbb{R}^d$, where $V$ has a $β$-Lipschitz gradient but need not be convex.Nonparametric Intensity Estimation for Covariate-Driven Poisson Processes Using Deep Learning
We consider nonparametric maximum likelihood estimation of the intensity function of a Poisson process, allowing the intensity to depend on observed covariates.StreamLTT: Risk Certification for Policy Selection under Stream Dependence
Learn-then-Test (LTT) provides a general framework for certifying deployment policies against a prescribed risk target, but its standard formulation targets a fixed population risk estimated from…Transport for Conditional Sampling: Foundations and Applications
Conditional sampling is central to Bayesian inference, generative modeling, and causal inference: given a joint law for random variables $X$ and $Y$ and an observation $Y=y$, one seeks to sample…Instance-Specific Sharp Convergence Rate of the Add-$λ$ Estimator via Feature Space Decomposition
Using the Feature Space Decomposition framework, we establish non-asymptotic matching upper and lower bounds for the expected Kullback-Leibler estimation error of the Add-$λ$ estimator of a discrete…Federated Estimation of Loading Spaces in Tensor Factor Models under Differential Privacy
Tensor factor models provide low-dimensional representations of tensor-valued observations while preserving their multiway structure.