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
Statistical inference for rough volatility: Minimax Theory Carsten Chong, Marc Hoffmann, Yanghui Liu, Mathieu Rosenbaum, and Gregoire Szymanski
Optimal parameter estimation for linear SPDEs from multiple measurements Randolf Altmeyer, Anton Tiepner, and Martin Wahl
Dimension-Free Mixing Times of Gibbs Samplers for Bayesian Hierarchical Models Filippo Ascolani and Giacomo Zanella
Reconciling model-X and doubly robust approaches to conditional independence testing Ziang Niu, Abhinav Chakraborty, Oliver Dukes, and Eugene Katsevich
Non-independent component analysis Geert Mesters and Piotr Zwiernik
Optimal Estimation of Schatten Norms of a rectangular Matrix Solène Thépaut and Nicolas Verzelen
Spectral Statistics of Sample Block Correlation Matrices Zhigang Bao, Jiang Hu, Xiaocong Xu, and Xiaozhuo Zhang
Distributed Estimation and Inference for Semi-parametric Binary Response Models Xi Chen, Wenbo Jing, Weidong Liu, and Yichen Zhang
Higher-Order Coverage Errors of Batching Methods via Edgeworth Expansions on t-Statistics Shengyi He and Henry Lam
On block-wise and reference panel-based estimators for genetic data prediction in high dimensions Bingxin Zhao, Shurong Zheng, and Hongtu Zhu
Plugin Estimation of Smooth Optimal Transport Maps Tudor Manole, Sivaraman Balakrishnan, Jonathan Niles-Weed, and Larry Wasserman
Change point inference in high-dimensional regression models under temporal dependence Haotian Xu, Daren Wang, Zifeng Zhao, and Yi Yu
High-dimensional covariance matrices under dynamic volatility models: asymptotics and shrinkage estimation Yi Ding and Xinghua Zheng
Change Acceleration and Detection Yanglei Song and Georgios Fellouris
Spectral regularized kernel two-sample tests Omar Hagrass, Bharath Sriperumbudur, and Bing Li
MARS via LASSO Dohyeong Ki, Billy Fang, and Adityanand Guntuboyina
Joint Sequential Detection and Isolation for Dependent Data Streams Anamitra Chaudhuri and Georgios Fellouris
Sharp adaptive and pathwise stable similarity testing for scalar ergodic diffusions Johannes Brutsche and Angelika Rohde
Estimation of the Spectral Measure from Convex Combinations of Regularly Varying Random Vectors Marco Oesting and Olivier Wintenberger
A Blockwise Empirical Likelihood Method for Time Series in Frequency Domain Inference Haihan Yu, Mark S. Kaiser, and Daniel J. Nordman
Nonparametric classification with missing data Torben Sell, Thomas B. Berrett, and Timothy I. Cannings
Deep Nonlinear Sufficient Dimension Reduction Yinfeng Chen, Yuling Jiao, Rui Qiu, and Zhou Yu
Locally Simultaneous Inference Tijana Zrnic and William Fithian
Spectral Analysis of Gram Matrices with Missing at Random Observations: Convergence, Central Limit Theorems, and Applications in Statistical Inference Huiqin Li, Guangming Pan, Yanqing Yin, and Wang Zhou
On the Approximation Accuracy of Gaussian Variational Inference Anya Katsevich and Philippe Rigollet
E-Statistics, Group Invariance and Anytime-Valid Testing Muriel Pérez-Ortiz, Tyron Lardy, Rianne De Heide, and Peter Grünwald
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
Heavy-tailed Bayesian nonparametric adaptation Sergios Agapiou and Ismael Castillo
Wald Tests When Restrictions Are Locally Singular Jean-Marie Dufour, Eric Renault, and Victoria Zinde-Walsh
Optimal Policy Evaluation Using Kernel-Based Temporal Difference Methods Yaqi Duan, Mengdi Wang, and Martin J Wainwright
Fundamental Limits of Low-Rank Matrix Estimation with Diverging Aspect Ratios Andrea Montanari and Yuchen Wu
Asymptotic Normality and Optimality in Nonsmooth Stochastic Approximation Damek Davis, Dmitriy Drusvyatskiy, and Liwei Jiang
Bootstrap-Assisted Inference for Generalized Grenander-type Estimators Kenichi Nagasawa, Matias Damian Cattaneo, and Michael Jansson
One-Step Estimation of Differentiable Hilbert-Valued Parameters Alex Luedtke and Incheoul Chung
Sharp multiple testing boundary for sparse sequences Ismael Castillo, Kweku Abraham, and Etienne Roquain
A Nonparametric Doubly Robust Test for a Continuous Treatment Effect Charles Raouf Doss, Guangwei Weng, Lan Wang, Ira Moscovice, and Tongtan Chantarat
Gromov-Wasserstein Distances: Entropic Regularization, Duality, and Sample Complexity Zhengxin Zhang, Ziv Goldfeld, Youssef Mroueh, and Bharath K. Sriperumbudur
Improved Covariance Estimation: Optimal Robustness and Sub-Gaussian Guarantees Under Heavy Tails Roberto Imbuzeiro Oliveira and Zoraida Fernandez Rico
Time-Uniform Central Limit Theory and Asymptotic Confidence Sequences Ian Waudby-Smith, David Arbour, Ritwik Sinha, Edward Kennedy, and Aaditya Ramdas
Debiased Inverse Propensity Score Weighting for Estimation of Average Treatment Effects with High-Dimensional Confounders Yuhao Wang and Rajen Shah
Online Change-point Detection for Matrix-valued Time Series with Latent Two-way Factor Structure Yong He, Xinbing Kong, Lorenzo Trapani, and Long Yu
Majority Vote for Distributed Differentially Private Sign Selection Weidong Liu, Jiyuan Tu, Xiaojun Mao, and Xi Chen
Tensor Factor Model Estimation by Iterative Projection Yuefeng Han, Rong Chen, Dan Yang, and Cun-Hui Zhang
Wasserstein Convergence in Bayesian and Frequentist Deconvolution Models Judith Rousseau and Catia Scricciolo
Detection and Estimation of Structural Breaks in High-Dimensional Functional Time Series Degui Li, Runze Li, and Han Lin Shang
Efficient Functional Lasso Kernel Smoothing for High-dimensional Additive Regression Eun Ryung Lee, Seyoung Park, Enno Mammen, and Byeong U Park
Learning Gaussian Mixtures Using the Wasserstein-Fisher-Rao Gradient Flow Yulang Yan, Kaizheng Wang, and Philippe Rigollet
Statistical Inference for Four-Regime Segmented Regression Models Han Yan and Song X Chen