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.
Get the few papers worth your time in Statistics Theory every morning.
Follow Statistics Theory and ReadSearch reads its new papers each day, then brings you the few worth reading, each with its key finding in a sentence.
Newest papers, page 8
Minimax Rates for Estimating Powered Sliced Wasserstein Functionals
We study the two-sample estimation of the powered Sliced Wasserstein functional under projected $r$-th moment assumptions.Life after delisting: tracking the publication output and citation impact of Scopus-discontinued journals with OpenAlex
Selective databases such as Scopus periodically re-evaluate the titles they index and remove those that no longer meet their quality criteria.Reflexivity of Grothendieck Spaces and Strongly Normal Sequences
A normalized sequence $(y_n)$ in the dual $E^{*}$ of a Banach space $E$ is called normal if it is weak-star null, and strongly normal if [ G(y_n)=\left{x\in…Witnessing Quantum Bayesian Inference beyond Classical Learning
Can an agent's predictions reveal whether its reasoning is based on a classical or a quantum model?The Geometry of Anisotropic Dilation for Optimal Regularization
A central question in data-driven inverse problems is how to construct a regularizer that adapts to the geometry of the data distribution.Finite-Rank Logistic Gaussian Processes with Exact Likelihood for Conditional Density Estimation
Conditional density estimation describes how the entire distribution of a response changes with covariates, and in imaging studies also with location.The Silhouette Operator: Identifiability of Low-Rank Measures from One-Dimensional Projections
Structured recovery phenomena, such as restricted isometry properties in compressed sensing, have shown that high-dimensional objects can often be reconstructed from remarkably low-dimensional…Unbounded Characteristic and Universal Kernels
Kernel methods are among the most powerful tools in machine learning and statistics, with a large number of successful applications.Fluctuations of Nonlinear Observables in Mean Field Neural Network Training
Mean field limits describe the training dynamics of wide neural networks through the evolution of the empirical distribution of their parameters.Outperformance Inverse Optimization: Learning Objective Functions that Outperform Agent Decisions
Inverse optimization estimates the weights of an objective function that explain observed decisions as optimal solutions, and is used in a variety of fields.Sharp Asymptotic Theory of Maximum Likelihood Estimation for Gaussian Processes with an RBF Kernel
Gaussian processes (GPs) are widely used across machine learning, spatial statistics, time-series analysis, optimization, Bayesian statistics, and scientific applications.Policy Learning with Weak Signals
Policy learning in digital experimentation faces three challenges: weak signal-to-noise ratios, rich covariate spaces, and massive data volumes.RoBART: Bayesian Additive Regression Trees with Tree-Specific Rotations
Bayesian additive regression trees (BART) can require many splits to approximate boundaries misaligned with the predictor axes.Settling the Sample Complexity of Rényi Entropy Estimation
Rényi entropy estimation has been comprehensively investigated by Acharya, Orlitsky, Suresh and Tyagi (SODA 2015; IEEE Trans.How Many Directions Must a Truncated Diffusion Sampler Retain? Matching Bounds Under Power-Law Spectra
Diffusion samplers can reduce computation by generating selected spectral coordinates and filling the remaining directions with noise.Vine Copula VAR:From Recursive Margins to Joint Forecast Inference
Joint-event forecasts often combine a dependence estimate based on past forecast errors with newly estimated marginal distributions.Asymptotic Analysis of Empirical Risk Minimization on Entry-wise i.i.d. Heavy-Tailed Data
Many real-world datasets exhibit unusually large values far more frequently than predicted by Gaussian models.Exact Calibration and Sharp Risk Geometry for Volume-Sampled Ridge Regression
We study ridge regression from exactly $s$ distinct rows of a fixed design.Detecting a Shift Is Not Enough: Exact Minimax Limits of Linear Representation Repair
A mean shift between two data sources can be easy to detect but hard to remove without substantially changing their representations.Efficient Posterior Sampling for $\mathbb Z_2$ Synchronization
Consider the $\mathbb Z_2$ synchronization problem \[ \boldsymbol{Y} = \fracλ{\sqrt n} θθ^{\top} + \boldsymbol{Z}, \] where $θ$ is uniform on $\{-1,1\}^n$ and $\boldsymbol{Z}$ is an independent…