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.

 

Testing network correlation efficiently via counting trees Cheng Mao, Yihong Wu, Jiaming Xu, and Sophie H. Yu
Non-independent component analysis Geert Mesters and Piotr Zwiernik
Estimation of the Spectral Measure from Convex Combinations of Regularly Varying Random Vectors Marco Oesting and Olivier Wintenberger
Statistical Complexity and Optimal Algorithms for Non-linear Ridge Bandits Nived Rajaraman, Yanjun Han, Jiantao Jiao, and Kannan Ramchandran
Tensor-on-Tensor Regression: Riemannian Optimization, Over-parameterization, Statistical-computational Gap, and Their Interplay Yuetian Luo and Anru R Zhang
Wald Tests When Restrictions Are Locally Singular Jean-Marie Dufour, Eric Renault, and Victoria Zinde-Walsh
Time-Uniform Central Limit Theory and Asymptotic Confidence Sequences Ian Waudby-Smith, David Arbour, Ritwik Sinha, Edward Kennedy, and Aaditya Ramdas
Tensor Factor Model Estimation by Iterative Projection Yuefeng Han, Rong Chen, Dan Yang, and Cun-Hui Zhang
Statistical Inference for Four-Regime Segmented Regression Models Han Yan and Song X Chen
Large Dimensional Independent Component Analysis: Statistical Optimality and Computational Tractability Arnab Auddy and Ming Yuan
Stereographic Markov Chain Monte Carlo Jun Yang, Krzysztof Latuszynski, and Gareth O Roberts
Skewed Bernstein-Von Mises Theorem and Skew-Modal Approximations Daniele Durante, Francesco Pozza, and Botond Szabo
Empirical partially Bayes multiple testing and compound $\chi^2$ decisions Nikolaos Ignatiadis and Bodhisattva Sen
Deep Neural Networks for Nonparametric Interaction Models with Diverging Dimension Sohom Bhattacharya, Jianqing Fan, and Debarghya Mukherjee
On the Statistical Complexity of Sample Amplification Brian Axelrod, Shivam Garg, Yanjun Han, Vatsal Sharan, and Gregory Valiant
Convex Regression in Multidimensions: Suboptimality of Least Squares Estimators Gil Kur, Fuchang Gao, Adityanand Guntuboyina, and Bodhisattva Sen
Noisy Recovery From Random Linear Observations: Sharp Minimax Rates Under Elliptical Constraints Reese Pathak, Martin J Wainwright, and Lin Xiao
The Projected Covariance Measure for Assumption-Lean Variable Significance Testing Anton Rask Lundborg, Ilmun Kim, Rajen Shah, and Richard Samworth
A Common-Cause Principle for Eliminating Selection Bias in Causal Estimands Through Covariate Adjustment Maya Mathur, Ilya Shpitser, and Tyler VanderWeele
Dimension Free Ridge Regression Chen Cheng and Andrea Montanari
Near-Optimal Inference in Adaptive Linear Regression Koulik Khamaru, Yash Deshpande, Tor Lattimore, Lester Mackey, and Martin J. Wainwright
Change Point Analysis With Irregular Signals Tobias Kley, Yuhan Philip Liu, Hongyuan Cao, and Wei Biao Wu
Statistical Inference for Decentralized Federated Learning Jia Gu and Song X Chen
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
Increasing Dimension Asymptotics for Two-Way Crossed Mixed Effect Models Ziyang Lyu, Scott A Sisson, and Alan H Welsh
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