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.

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
Asymptotic Distributions of Largest Pearson Correlation Coefficients under Dependent Structures Tiefeng Jiang and Tuan Pham
Entropic Covariance Models Piotr Zwiernik
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
Semi-Parametric Inference Based on Adaptively Collected Data Licong Lin, Koulik Khamaru, and Martin J. Wainwright
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
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
Erratum: Quantile Processes and Their Applications in Finite Populations Anurag Dey and Probal Chaudhuri
Strong Approximations for Empirical Processes Indexed by Lipschitz Functions Ruiqi (Rae) Yu and Matias D. Cattaneo
Testing Stationarity and Change Point Detection in Reinforcement Learning Mengbing Li, Chengchun Shi, Zhenke Wu, and Piotr Fryzlewicz
Adaptive Estimation of the 𝕃2-Norm of a Probability Density and Related Topics I. Lower Bounds. Galatia Cleanthous, Athanasios Georgiadis, and Oleg Lepski
Adaptive Estimation of the 𝕃2-Norm of a Probability Density and Related Topics II. Upper Bounds via the Oracle Approach. Galatia Cleanthous, Athanasios Georgiadis, and Oleg Lepski
Asymptotic Distribution of Maximum Likelihood Estimator in Generalized Linear Mixed Models with Crossed Random Effects Jiming Jiang
On the Structural Dimension of Sliced Inverse Regression Dongming Huang, Songtao Tian, and Qian Lin
Semiparametric Modeling and Analysis for Longitudinal Network Data Yinqiu He, Jiajin Sun, Yuang Tian, Zhiliang Ying, and Yang Feng
Self-Normalized Cramér Type Moderate Deviation Theorem for Gaussian Approximation Jingkun Qiu, Song Xi Chen, and Qi-Man Shao
On the Multiway Principal Component Analysis Jialin Ouyang and Ming Yuan
Semiparametric Adaptive Estimation Under Informative Sampling Kosuke Morikawa, Yoshikazu Terada, and Jae Kwang Kim
Algorithmic Stability Implies Training-Conditional Coverage for Distribution-Free Prediction Methods Ruiting Liang and Rina Foygel Barber
Policy Learning “Without” Overlap: Pessimism and Generalized Empirical Bernstein’s Inequality Ying Jin, Zhimei Ren, Zhuoran Yang, and Zhaoran Wang
Reinforcement Learning for Individual Optimal Policy From Heterogeneous Data Rui Miao, Babak Shahbaba, and Annie Qu
Debiased Regression Adjustment in Completely Randomized Experiments With Moderately High-Dimensional Covariates Xin Lu, Fan Yang, and Yuhao Wang
Asymptotic Theory of Geometric and Adaptive $k$-Means Clustering Adam Quinn Jaffe
Fixed and Random Covariance Regression Analyses Tao Zou, Wei Lan, Runze Li, and Chih-Ling Tsai
Spectral Gap Bounds for Reversible Hybrid Gibbs Chains Qian Qin, Nianqiao Ju, and Guanyang Wang
Low-Degree Hardness of Detection for Correlated Erdős-Rényi Graphs Jian Ding, Hang Du, and Zhangsong Li
Optimal Vintage Factor Analysis With Deflation Varimax Xin Bing, Xin He, Dian Jin, and Yuqian Zhang
Counterfactual Inference in Sequential Experiments Raaz Dwivedi, Katherine Tian, Sabina Tomkins, Predrag Klasnja, Susan Murphy, and Devavrat Shah
Higher-Order Entrywise Eigenvectors Analysis of Low-Rank Random Matrices: Bias Correction, Edgeworth Expansion, and Bootstrap Fangzheng Xie and Yichi Zhang
The Functional Graphical Lasso Kartik Govind Waghmare, Tomas Masak, and Victor Michael Panaretos
Deep Horseshoe Gaussian Processes Ismaël Castillo and Thibault Christophe Randrianarisoa
High-Dimensional Statistical Inference for Linkage Disequilibrium Score Regression and Its Cross-Ancestry Extensions Fei Xue and Bingxin Zhao
Optimal and Exact Recovery on the General Non-Uniform Hypergraph Stochastic Block Model Ioana Dumitriu and Hai-Xiao Wang
Structured Matrix Learning under Arbitrary Entrywise Dependence and Estimation of Markov Transition Kernel Jinhang Chai and Jianqing Fan
Online Statistical Inference in Decision Making with Matrix Context Qiyu Han, Will Wei Sun, and Yichen Zhang
Estimation and Inference in Distributional Reinforcement Learning Liangyu Zhang, Yang Peng, Jiadong Liang, Wenhao Yang, and Zhihua Zhang
Advances in Bayesian Model Selection Consistency for High-Dimensional Generalized Linear Models Jeyong Lee, Minwoo Chae, and Ryan Martin
Symmetry: A General Structure in Nonparametric Regression Louis Goldwater Christie and John A. D. Aston
A Unified Analysis of Likelihood-based Estimators in the Plackett–Luce Model Ruijian Han and Yiming Xu
Theory of Functional Principal Component Analysis for Discretely Observed Data Hang Zhou, Dongyi Wei, and Fang Yao
The High-Dimensional Asymptotics of Principal Component Regression Alden Green and Elad Romanov
Online Estimation and Inference for Robust Policy Evaluation in Reinforcement Learning Weidong Liu, Jiyuan Tu, Yichen Zhang, and Xi Chen
Robust Transfer Learning with Unreliable Source Data Jianqing Fan, Cheng Gao, and Jason Matthew Klusowski
Pseudo-Likelihood-Based M-Estimation of Random Graphs With Dependent Edges and Parameter Vectors of Increasing Dimension Jonathan Roy Stewart and Michael Schweinberger
Trimmed Sample Means for Robust Uniform Mean Estimation and Regression Roberto Imbuzeiro Moraes Felinto de Oliveira, and Lucas Resende
Improved Learning Theory for Kernel Distribution Regression With Two-Stage Sampling François Bachoc, Louis Béthune, Alberto González-Sanz, and Jean-Michel Loubes
Yurinskii’s Coupling for Martingales Matias Damian Cattaneo, Ricardo Pereira Masini, and William George Underwood
Ear Optimal Sample Complexity for Matrix and Tensor Normal Models via Geodesic Convexity Rafael Mendes de Oliveira, William Cole Franks, Akshay Ramachandran, and Michael Walter
Tests of Missing Completely at Random Based on Sample Covariance Matrices Alberto Bordino and Thomas Benjamin Berrett
Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning Yihong Gu, Cong Fang, Peter Bühlmann, and Jianqing Fan
Spectral Density Estimation of Function-Valued Spatial Processes Rafail Kartsioukas, Stilian Stoev, and Tailen Hsing
Improving Knockoffs With Conditional Calibration Yixiang Luo, William Fithian, and Lihua Lei
Entrywise Dynamics and Universality of General First Order Methods Qiyang Han
Efficiently Matching Random Inhomogeneous Graphs via Degree Profiles Jian Ding, Yumou Fei, and Yuanzheng Wang
Clustering risk in Non-parametric Hidden Markov and I.I.D. Models Elisabeth Gassiat, Ibrahim Kaddouri, and Zacharie Naulet
Causal Effect Estimation Under Network Interference With Mean-Field Methods Sohom Bhattacharya and Subhabrata Sen
The Empirical Copula Process in High Dimensions: Stute’s Representation and Applications Axel Bücher and Cambyse Pakzad
Scalable Inference in Functional Linear Regression With Streaming Data Jinhan Xie, Enze Shi, Peijun Sang, Zuofeng Shang, Bei Jiang, and Linglong Kong
Semi-Supervised U-Statistics Ilmun Kim, Larry Wasserman, Sivaraman Balakrishnan, and Matey Neykov
Sparse PCA: A New Scalable Estimator Based on Integer Programming Kayhan Behdin and Rahul Mazumder
Rank Tests for PCA Under Weak Identifiability Davy Paindaveine, Laura Peralvo Maroto, and Thomas Verdebout