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
Asymptotically-Exact Selective Inference for Quantile Regression Yumeng Wang, Snigdha Panigrahi, and Xuming He
Dualizing Le Cam’s method for functional estimation I: General theory Yury Polyanskiy and Yihong Wu
Erratum: Quantile Processes and Their Applications in Finite Populations Anurag Dey and Probal Chaudhuri
On the Structural Dimension of Sliced Inverse Regression Dongming Huang, Songtao Tian and Qian Lin
Counterfactual Inference in Sequential Experiments Raaz Dwivedi, Katherine Tian, Sabina Tomkins, Predrag Klasnja, Susan Murphy and Devavrat Shah
Pseudo-Likelihood-Based M-Estimation of Random Graphs with Dependent Edges and Parameter Vectors of Increasing Dimension Jonathan Roy Stewart and Michael Schweinberger
Near Optimal Sample Complexity for Matrix and Tensor Normal Models via Geodesic Convexity Rafael Mendes de Oliveira, William Cole Franks, Akshay Ramachandran and Michael Walter
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
A Flexible Defense Against the Winner’s Curse Tijana Zrnic and William Fithian
Clustering by Hill-Climbing: Consistency Results Ery Arias-Castro and Wanli Qiao
Kurtosis-Based Projection Pursuit for Matrix-Valued Data Una Radojicic, Klaus Nordhausen and Joni Virta
A Geometrical Analysis of Kernel Ridge Regression and its Applications Zong Shang, Guillaume Lecué and Georgios Gavrilopoulos
A Two-Way Heterogeneity Model for Dynamic Networks Binyan Jiang, Chenlei Leng, Ting Yan, Qiwei Yao and Xinyang Yu
Optimal Sequencing Depth for Single-Cell RNA-Sequencing in Wasserstein Space Jakwang Kim, Sharvaj Kubal and Geoffrey Schiebinger
High-Dimensional Hilbert-Schmidt Linear Regression with Hilbert Manifold Variables Changwon Choi and Byeong U. Park
Poisson Empirical Bayes Estimation: When Does g-Modeling Beat f-Modeling in Theory (And in Practice)? Yandi Shen and Yihong Wu
Online Estimation with Rolling Validation: Adaptive Nonparametric Estimation with Streaming Data Tianyu Zhang and Jing Lei
Fundamental Limits of Community Detection From Multi-View Data: Multi-Layer, Dynamic and Partially Labeled Block Models Xiaodong Yang, Buyu Lin and Subhabrata Sen
Average Partial Effect Estimation Using Double Machine Learning Harvey Klyne and Rajen Shah
Solving the Poisson Equation Using Coupled Markov Chains Pierre Etienne Jacob, Randal Douc, Anthony Lee and Dootika Vats
A Computational Transition for Detecting Correlated Stochastic Block Models by Low-Degree Polynomials Guanyi Chen, Jian Ding, Shuyang Gong and Zhangsong Li
Pseudo-Labeling for Kernel Ridge Regression under Covariate Shift Kaizheng Wang
Change Point Estimation for a Stochastic Heat Equation Markus Reiß, Claudia Strauch,and Lukas Trottner
Multivariate Root-N-Consistent Smoothing Parameter Free Matching Estimators and Estimators of Inverse Density Weighted Expectations Hajo Holzmann and Alexander Meister
Berry-Esseen Bounds for Design-Based Causal Inference With Possibly Diverging Treatment Levels and Varying Group Sizes Peng Ding and Lei Shi
Neural Networks Generalize on Low Complexity Data Sourav Chatterjee and Timothy Sudijono
Semiparametric Bernstein-Von Mises Phenomenon via Isotonized Posterior in Wicksell’s Problem Francesco Gili, Geurt Jongbloed and Aad van der Vaart
Optimal Convex $M$-Estimation via Score Matching Oliver Y. Feng, Yu-Chun Kao, Min Xu and Richard J. Samworth
Communication-Efficient and Distributed-Oracle Estimation for High-Dimensional Quantile Regression Songshan Yang, Yifan Gu, Hanfang Yang and Xuming He
Adaptive Robust Confidence Intervals Yuetian Luo and Chao Gao
Optimality of Approximate Message Passing for Spiked Matrix Models with Rotationally Invariant Noise Rishabh Dudeja, Songbin Liu and Junjie Ma
Information Theoretic Limits of Robust Sub-Gaussian Mean Estimation Under Star-Shaped Constraints Akshay Prasadan and Matey Neykov
Confounder Selection via Iterative Graph Expansion F. Richard Guo and Qingyuan Zhao
Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation Zhenyu Wang, Peter Bühlmann and Zijian Guo
Trace Test for High-Dimensional Cointegration Alexei Onatski and Chen Wang
Estimation of Grouped Time-Varying Network Vector Autoregressive Models Degui Li, Bin Peng, Songqiao Tang and Wei Biao Wu
Large-Scale Multiple Testing: Fundamental Limits of False Discovery Rate Control and Compound Oracle Yutong Nie and Yihong Wu
Analysis of Singular Subspaces Under Random Perturbations Ke Wang
Versatile Differentially Private Learning for General Loss Functions Song X Chen, Qilong Lu and Yumou Qiu
Optimal Eigenvalue Shrinkage in the Semicircle Limit Michael Jacob Feldman and David Leigh Donoho
Nonparametric Estimation of a Covariate-Adjusted Counterfactual Treatment Regimen Response Curve Ashkan Ertefaie, Luke Duttweiler, Brent A. Johnson and Mark J. Van Der Laan
Spectrum-Aware Debiasing: A Modern Inference Framework with Applications to Principal Components Regression Yufan Li and Pragya Sur
Scalable inference for Nonparametric Stochastic Approximation in Reproducing Kernel Hilbert Spaces Meimei Liu, Zuofeng Shang and Yun Yang
Online Tensor Learning: Computational and Statistical Trade-offs, Adaptivity and Optimal Regret Jingyang Li, Jian-Feng Cai, Yang Chen and Dong Xia
Statistical Inference for Low-Rank Tensors: Heteroskedasticity, Subgaussianity, and Applications Joshua Agterberg and Anru Zhang
Precise Asymptotics of Bagging Regularized M-estimators Takuya Koriyama, Pratik Patil, Jin-Hong Du, Kai Tan and Pierre C. Bellec
Finite- and large-sample inference for model and coefficients in high-dimensional linear regression with repro samples Peng Wang, Minge Xie and Linjun Zhang
Inferring the dependence graph density of binary graphical models in high dimension Julien Chevallier, Eva Löcherbach and Guilherme Ost
Eigenvector Overlaps in Large Sample Covariance Matrices and Nonlinear Shrinkage Estimators Guangming Pan and Zeqin Lin
Learning extremal graphical structures in high dimensions Sebastian Engelke, Michael Lalancette and Stanislav Volgushev
Object detection under the linear subspace  model with application to cryo-EM images Amitay Eldar, Keren Mor Waknin, Samuel Davenport, Tamir Bendory, Armin Schwartzman and Yoel Shkolnisky
PCA for Point Processes Franck Picard, Vincent Rivoirard, Angelina Roche and Victor Panaretos
Parameter identification in linear non-Gaussian causal models under general confounding Daniele Tramontano, Jalal Etesami and Mathias Drton
Generalized Multilinear Models for Sufficient Dimension Reduction on Tensor-valued Predictors Daniel Kapla and Efstathia Bura
The out-of sample prediction error of the $\sqrt{\text{LASSO}}$ and related estimators José Luis Montiel Olea, Cynthia Rush, Amilcar Velez and Johannes Wiesel
Gradient descent inference in empirical risk minimization Qiyang Han and Xiaocong Xu
Generalized Linear Spectral Statistics of High-dimensional Sample Covariance Matrices and Its Applications Yanlin Hu, Qing Yang and Xiao Han
Reviving pseudo-inverses: Asymptotic properties of large dimensional Moore-Penrose and Ridge-type inverses with applications Taras Bodnar and Nestor Parolya
Statistical-Computational Trade-offs for Recursive Adaptive Partitioning Estimators Yan Shuo Tan, Jason M. Klusowski and Krishnakumar Balasubramanian
VECCHIA GAUSSIAN PROCESSES: ON PROBABILISTIC AND STATISTICAL PROPERTIES Botond Tibor Szabo and Yichen Zhu
Adaptive Bayesian regression on data with low intrinsic dimensionality Tao Tang, Xiuyuan Cheng, Nan Wu and David Dunson
Uncertainty quantification for iterative algorithms in linear models with application to early stopping Pierre C Bellec and Kai Tan
Markov stick-breaking processes Maria F. Gil-Leyva, Antonio Lijoi, Ramses H. Mena and Igor Pruenster
Identification and estimation for matrix time series CP-factor models Jinyuan Chang, Yue Du, Guanglin Huang and Qiwei Yao
Towards a Unified Theory for Semiparametric Data Fusion with Individual-Level Data Ellen Sandra Graham, Marco Carone and Andrea Rotnitzky
A Two-step Estimating Approach for Heavy-tailed AR Models with Non-zero Median GARCH-type Noises She Rui, Dai Linlin and Ling Shiqing
Erratum: Edgeworth Expansions for Linear Rank Statistics Walter Schneller