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, page 4
Approximation Families and the Statistical Efficiency of Denoising Score Matching
We study score matching over finite-dimensional approximation spaces generated by gradients of eigenfunctions of the infinitesimal generator of a forward diffusion.Minimax Regret for Sobolev Kernel-Based Reinforcement Learning
We study the minimax regret rate for episodic kernel-based reinforcement learning, where the expected reward and transition density belong to a Sobolev reproducing kernel Hilbert space, with…Inference for Causal Path-Specific Effects in Longitudinal Studies with Repeated Treatments and Multiple Mediators
In longitudinal studies, repeated treatments may be followed by different sequences of mediators and time-varying covariates.Intrinsic Gradient Barriers in Pushforward Deep Generative Models
Broad classes of deep generative models (DGMs) operate by learning to map a simple source distribution, such as a standard Gaussian, to a complex high-dimensional target distribution.The German Tank Problem & Navigating by Scent
A robot searching for the source of an odor must eventually answer a notably hard question: have I arrived?Faithful Federated Tabular Data Synthesis
Synthetic data is an important data source in the AI era.Online Covariance Estimation for Averaged Stochastic Gradient Descent on Riemannian Manifolds
We study online inference for averaged Riemannian stochastic gradient descent (RSGD) when the unknown parameter lies on a manifold.On the Error Analysis of Triangular Transport Maps for Conditional Generation via MMD-based Objectives
Conditional generative models parameterized by triangular transport maps are widely used for conditional sampling, yet theoretical guarantees on the quality of the conditional measures they produce…Task-Directed Spectral Design for Local Quantum Tomography
Many tomography tasks require only a low-dimensional function of the state.Exact Global Minimax Risk near Nonidentification
We study squared-error estimation when a nuisance parameter is unidentified on a submodel but affects a target that vanishes there.Score-Preserving Continual Lasso under Task Shift
Continual learning (CL) aims to learn from sequential tasks while mitigating catastrophic forgetting when historical data are no longer accessible.Not All Mislabels Are Equal: Efficient Task-Aware Auditing with Confidence Sequences
We study efficient, adaptive auditing of categorical labels in a fixed dataset, motivated by biodiversity-credit programs in which expert verification of species records is costly and downstream…Sharp Bounds on Continuous Treatment Effects under Unobserved Confounding
We study partial identification of conditional treatment effects with a continuous treatment under unobserved confounding.Deep Learning for Random Objects in Metric Spaces: Beyond Manifold Assumptions
Deep learning (DL) methods have substantially improved predictive performance across a wide range of downstream prediction tasks, enabling new discoveries in multiple domains.Dimension-free regimes for unadjusted Langevin in ridge-separable models: A subspace robust Wasserstein analysis
The Unadjusted Langevin Algorithm (ULA) is widely used for high-dimensional Bayesian inference.Phase Transition from Random Features to Fully Trainable Networks
Constructing accurate and computationally tractable approximation spaces is a fundamental task in scientific computing.Sharp Bounds on the Winner's Bias under Independent and Nonnegatively Correlated Noise
Reporting the best of $M$ noisy, unbiased scores overstates the winner's value by up to $c\sqrt{V}$, where $V$ is the total noise variance and the sharp constant $c$ depends on how the noise is…A unified approach for estimation of unnormalized models via Stein operators
In this paper, we develop a unified framework connecting proper scoring rules and Stein's method for the estimation of unnormalized models.Asymmetric Likelihood-Free Hypothesis Testing
In likelihood-free hypothesis testing (LFHT), given samples from two unknown distributions $P,Q$ on $[n]$ with $\mathrm{d_{TV}}(P,Q)\geq\varepsilon$, and from a third distribution $D\in\{P,Q\}$, the…Existence of Minimizers for the Gaussian Mumford-Shah Model via Covariance Floors on the Value Distribution
The Gaussian Mumford-Shah model segments an image into regions by minimizing a continuous multivariate Gaussian negative log-likelihood term plus a perimeter penalty.