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.

Erratum: Quantile Processes and Their Applications in Finite Populations Anurag Dey and Probal Chaudhuri
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
Adaptive Robust Confidence Intervals Yuetian Luo and Chao Gao
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
A non-asymptotic distributional theory of approximate message passing for sparse and robust regression Gen Li and Yuting Wei
Test of Independence Using Generalized Distance Correlation Jianqing Fan, Zhipeng Lou and Danna Zhang
Attainability of Two-Point Testing Rates for Finite-Sample Location Estimation Spencer Compton and Gregory Valiant
Minimax optimal seriation in polynomial time Yann Issartel, Christophe Giraud and Nicolas Verzelen
Dual Induction CLT for High-dimensional m-dependent Data Heejong Bong, Arun Kumar Kuchibhotla and Alessandro Rinaldo
Statistical Inference in Tensor Completion: Optimal Uncertainty Quantification and Statistical-to-Computational Gaps Wanteng Ma and Dong Xia
A novel statistical approach to analyze image classification Juntong Chen, Sophie Langer and Johannes Schmidt-Hieber
Approximate independence of permutation mixtures Yanjun Han and Jonathan Niles-Weed
Optimal Integrative Estimation for Distributed Precision Matrices with Heterogeneity Adjustment Yinrui Sun and Yin Xia
Local geometry of high-dimensional mixture models: Effective spectral theory and dynamical transitions Gerard Ben Arous, Reza Gheissari, Jiaoyang Huang and Aukosh Jagannath
High-order Accurate Inference on Manifolds Chengzhu Huang and Anru Zhang
Uniform Estimation and Inference for Nonparametric Partitioning-Based M-Estimators Matias D. Cattaneo, Yingjie Feng and Boris Shigida
DiPMInd: Distance Profile based Mutual Independence testing for random objects Yaqing Chen and Paromita Dubey
Estimating the False Discovery Rate of Variable Selection Yixiang Luo, William Fithian and Lihua Lei
Improved thresholds for e-values Christopher Blier-Wong and Ruodu Wang
Measuring Evidence against Exchangeability and Group Invariance with E-values Nick Koning
Privacy Guarantees in Posterior Sampling under Contamination Shenggang Hu, Louis Aslett, Hongsheng Dai, Murray Pollock and Gareth Owen Roberts
Meta-Learning with Generalized Ridge Regression: High-dimensional Asymptotics, Optimality and Hyper-covariance Estimation Yanhao Jin, Krishnakumar Balasubramanian and Debashis Paul
Unbiased kinetic Langevin Monte Carlo with inexact gradients Neil K. Chada, Benedict Leimkuhler, Daniel Paulin and Peter Archibald Whalley
Quasi-Monte Carlo confidence intervals using quantiles of randomized nets Zexin Pan