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 11
When Noise Estimation Hides Basis Misspecification in Repeated Bayesian Inverse Problems
Basis-restricted priors in Bayesian inverse problems can lose coverage when the truth has components outside the basis.Search Dimension in Unlabeled Projection Pursuit: A Scaling Law for Subspace Restriction
Projection pursuit searches for a direction along which the data look least Gaussian.Mutual Information Constrained Chernoff Bottleneck
The classical information bottleneck (IB) measures the relevance of a representation $U$ of $X$ to a target $Y$ by $I(U;Y)$, which does not directly characterize the error of downstream decisions.Local polynomial density ratio estimation
We propose a novel local-polynomial estimator of the ratio $r=f/g$ of two $d$-dimensional densities $f$ and $g$, from which independent samples are available.Grokking through the Lens of Minimum-Norm Interpolation
Grokking shows that fitting the training data and learning the underlying signal can occur at very different stages.Bandits with Multiple Optimal Arms: Minimax Regret and Non-Adaptivity
We study multi-armed bandits (MAB) with multiple optimal arms, motivated by the fact that many practical decision making problems admit multiple correct answers.Singularities of Non-negative Matrix Factorization and their application to Bayesian inference
Non-negative matrix factorization (NMF) is a singular statistical model whose Bayesian asymptotics are governed by the real log canonical threshold (RLCT).Understanding Generalization Requires Universal Induction
Classical statistical theory is insufficient to explain the successes of general-purpose AI models, because it depends on handcrafted inductive biases that it cannot justify.Isotonic surrogate modeling for computer experiments with many input variables
Virtual simulators are widely used for studying complex physical phenomena, from particle collisions to rocket propulsion.Finite-Sample Theory for Fitted Q-Iteration When Actions Are Functions
Offline reinforcement learning seeks optimal decision rules from previously collected data.The Statistical Benefits of Multiple Responses for Learning from Demonstrations
Many generative systems return multiple candidate responses and are evaluated according to the best one.Identifying the Predictable Drift of a Semimartingale from Marginal Laws
A special semimartingale admits a unique decomposition $X=X_0+M+A$ into a local martingale $M$ and a predictable finite-variation part $A$.Recovering Lower-Dimensional Semialgebraic Support of a Measure from its Moments
Recovering probability measures from their moments has numerous applications, esp. in connection with the method of moments in statistics and optimization.Sharp training-conditional coverage for conformal prediction under covariate shift
Weighted split conformal prediction reweights calibration scores by the likelihood ratio between the test and training covariate distributions and guarantees marginal coverage under covariate shift.Valid and Efficient Split Conformal Regression for Time Series
We study conformalized quantile regression and conformalized median regression that fit a model on one block of a time series and calibrate the conformal interval on the adjacent block.Conformal Coverage of Time Series: Validity and Inference
Conformal prediction provides marginal coverage guarantees, yet practitioners may wonder if the observed coverage is truly abnormal or consistent with sampling variation.Agnostic Smoothed Online Regression with Adversarial Responses
We study smoothed online prediction with bounded adversarial responses.Domain Adaptation with Target Information via Doubly-Anchored Distributionally Robust Optimization
Domain Adaptation (DA) often lacks worst-case guarantees, while Distributionally Robust Optimization (DRO) based only on source data centers its ambiguity set at the source law and ignores available…Sliced Orlicz-Wasserstein
We propose sliced Orlicz-Wasserstein (SOW) distance which is a generalization of sliced Wasserstein (SW) distance.Maximum Likelihood Estimation for Entity Ranking under Iterative Synthetic Data Augmentation
We study entity ranking and inference under the Bradley--Terry--Luce (BTL) model using pairwise comparisons collected over sparse comparison graphs.