Papers to Appear in Subsequent Issues

When papers are accepted for publication, they will appear below. Any changes that are made during the production process will only appear in the final version. Papers listed here are not updated during the production process and are removed once an issue is published.

Wald Tests When Restrictions Are Locally Singular Jean-Marie Dufour, Eric Renault, and Victoria Zinde-Walsh
Large Dimensional Independent Component Analysis: Statistical Optimality and Computational Tractability Arnab Auddy and Ming Yuan
Empirical partially Bayes multiple testing and compound $\chi^2$ decisions Nikolaos Ignatiadis and Bodhisattva Sen
A Common-Cause Principle for Eliminating Selection Bias in Causal Estimands Through Covariate Adjustment Maya Mathur, Ilya Shpitser, and Tyler VanderWeele
Near-Optimal Inference in Adaptive Linear Regression Koulik Khamaru, Yash Deshpande, Tor Lattimore, Lester Mackey, and Martin J. Wainwright
Concentration of Discrepancy-Based Approximate Bayesian Computation via Rademacher Complexity Sirio Legramanti, Daniele Durante, and Pierre Alquier
Forward Selection and Post-Selection Inference in Factorial Designs Lei Shi, Jingshen Wang, and Peng Ding
On the Sample Complexity of Entropic Optimal Transport Philippe Rigollet and Austin J Stromme
Deflated HeteroPCA: Overcoming the Curse of Ill-Conditioning in Heteroskedastic PCA Yuchen Zhou and Yuxin Chen
Nonlinear Global Fréchet Regression for Random Objects via Weak Conditional Expectation Satarupa Bhattacharjee, Bing Li, and Lingzhou Xue
Simplex Quantile Regression Without Crossing Tomohiro Ando and Ker-Chau Li
Bayesian Nonparametric Inference in McKean-Vlasov models Richard Nickl, Gregorios Pavliotis, and Kolyan Ray
Ensemble Projection Pursuit for General Nonparametric Regression Haoran Zhan, Mingke Zhang, and Yingcun Xia
Rate-Optimal Estimation of Mixed Semimartingales Carsten Chong, Thomas Delerue, and Fabian Mies
Asymptotic Distributions of Largest Pearson Correlation Coefficients under Dependent Structures Tiefeng Jiang and Tuan Pham
Some Theory About Efficient Dimension Reduction With Respect to Interaction Between Two Responses Wei Luo
Observable Adjustments in Single-Index Models for Regularized M-Estimators With Bounded p/n Pierre C. Bellec
Statistical Framework for Analyzing Shape in a Time Series of Random Geometric Objects Anne Margrete Nicolien van Delft and Andrew J. Blumberg
General Spatio-Temporal Factor Models for High-Dimensional Random Fields on a Lattice Matteo Barigozzi, Davide La Vecchia, and Hang Liu
Optimal Heteroskedasticity Testing in Nonparametric Regression Subhodh Kotekal and Soumyabrata Kundu
A Statistical Framework of Watermarks for Large Language Models: Pivot, Detection Efficiency and Optimal Rules Xiang Li, Feng Ruan, Huiyuan Wang, Qi Long, and Weijie Su
Approximation Error from  Discretizations and Its Applications Junlong Zhao, Xiumin Liu, Bin Du, and Yufeng Liu
Embedding Distributional Data Ery Arias-Castro and Wanli Qiao
Empirical Likelihood Based Testing for Multivariate Regular Variation John H.J. Einmahl, Andrea Krajina, and Juan Juan Cai
Computationally Efficient and Statistically Optimal Robust High-dimensional Linear Regression Yinan Shen, Jingyang Li, Jian-Feng Cai, and Dong Xia
Entropic Covariance Models Piotr Zwiernik
Multivariate Dynamic Mediation Analysis Under a Reinforcement Learning Framework Lan Luo, Chengchun Shi, Jitao Wang, Zhenke Wu, and Lexin Li
A New Central Limit Theorem for the Augmented IPW Estimator: Variance Inflation, Cross-fit Covariance and Beyond Kuanhao Jiang, Rajarshi Mukherjee, Subhabrata Sen, and Pragya Sur
Sparse Anomaly Detection Across Referentials: A Rank-Based Higher Criticism Approach Ivo V. Stoepker, Rui M. Castro, and Ery Arias-Castro
Studentized Tests of Independence: Random-Lifter Approach Gao Zhe, Roulin Wang, Xueqin Wang, and Heping Zhang
Residual Permutation Test for Regression Coefficient Testing Kaiyue Wen, Tengyao Wang, and Yuhao Wang
ARK: Robust Knockoffs Inference with Coupling Yingying Fan, Lan Gao, and Jinchi Lv
On the Convergence of Coordinate Ascent Variational Inference Anirban Bhattacharya, Debdeep Pati, and Yun Yang
Optimal Transport Map Estimation in General Function Spaces Vincent Divol, Jonathan Niles-Weed, and Aram-Alexandre Pooladian
Unified Algorithms for RL with Decision-Estimation Coefficients: PAC, Reward-Free, Preference-Based Learning, and Beyond Fan Chen, Song Mei, and Yu Bai
Low Coordinate Degree Algorithms I: Universality of Computational Thresholds for Hypothesis Testing Dmitriy Kunisky
Semi-Parametric Inference Based on Adaptively Collected Data Licong Lin, Koulik Khamaru, and Martin J. Wainwright
Deep Approximate Policy Iteration Jin Liu
The Numeraire E-Variable and Reverse Information Projection Martin Larsson, Aaditya Ramdas, and Johannes Ruf
Asymptotically-Exact Selective Inference for Quantile Regression Yumeng Wang, Snigdha Panigrahi, and Xuming He
The Generalization Error of Max-Margin Linear Classifiers: Benign Overfitting and High Dimensional Asymptotics in the Overparametrized Regime Andrea Montanari, Feng Ruan, Youngtak Sohn, and Jun Yan
Precise Error Rates for Computationally Efficient Testing Ankur Moitra and Alexander S. Wein
A Unifying Framework for Global Gaussianization: Asymptotic Equivalence of Locally Stationary Processes and Bivariate White Noise Cristina Butucea, Alexander Meister, and Angelika Rohde
A Duality Framework for Analyzing Random Feature and Two-Layer Neural Networks Hongrui Chen, Jihao Long, and Lei Wu
BELIEF in Dependence: Leveraging Atomic Linearity in Data Bits for Rethinking Generalized Linear Models Benjamin Brown, Kai Zhang, and Xiao-Li Meng
Sparsity Meets Correlation in Gaussian Sequence Model Subhodh Kotekal and Chao Gao
Conformal Inference for Random Objects Hang Zhou and Hans-Georg Müller
Statistical Algorithms for Low-Frequency Diffusion Data: A PDE Approach Matteo Giordano and Sven Wang
Minimax Rate for Multivariate Data Under Componentwise Local Differential Privacy Constraints Chiara Amorino and Arnaud Gloter
Dualizing Le Cam’s Method for Functional Estimation, With Applications to Estimating the Unseens Yury Polyanskiy and Yihong Wu