Papers to Appear in Subsequent Issues

Minimax  Estimation of Large Precision Matrices with Bandable Cholesky Factor Yu Liu and Zhao Ren
Testability of High-Dimensional Linear Models with Non-Sparse Structures Jelena Bradic, Jianqing Fan, and Yinchu Zhu
A shrinkage principle for heavy-tailed data: high-dimensional robust low-rank matrix recovery Jianqing Fan, Weichen Wang, and Ziwei Zhu
Consistent Nonparametric Estimation  for Heavy-tailed Sparse Graphs Christian Borgs, Jennifer T. Chayes, Henry Cohn, and Shirshendu Ganguly
Strong Selection Consistency of Bayesian Vector Autoregressive Models based on a Pseudo-Likelihood Approach George Michailidis
On cross-validated lasso in high dimensions Denis Chetverikov, Zhipeng Liao, and Victor Chernozhukov
Frame-constrained Total Variation Regularization for White Noise Regression Miguel del Alamo, Housen Li, and Axel Munk
Adaptive robust estimation in sparse vector model Laëtitia Comminges, Olivier Collier, Mohamed Ndaoud, and Alexandre B. Tsybakov
Non-exchangeable random partition models for microclustering Giuseppe Di Benedetto, Francois Caron, and Yee Whye Teh
Learning Models with Uniform Performance via Distributionally Robust Optimization John Christopher Duchi and Hongseok Namkoong
Second Order Stein: SURE for SURE and Other Applications in High-Dimensional Inference Pierre C. Bellec and Cun-Hui Zhang
Model Diagnostics in Time Series Regression Models Javier Hidalgo
Total positivity in exponential families with application to binary variables Steffen Lilholt Lauritzen, Caroline Uhler, and Piotr Zwiernik
Peskun-Tierney ordering for Markov chain and process Monte Carlo: beyond the reversible scenario Christophe Andrieu and Samuel Livingstone
A Causal Bootstrap Konrad Menzel and Guido Imbens
Principal components in linear mixed models with general bulk Zhou Fan, Yi Sun, and Zhichao Wang
Statistical inference in sparse high-dimensional additive models Enno Mammen, Karl Gregory, and Martin Wahl
A Convex Optimization Approach to High-dimensional Sparse Quadratic Discriminant Analysis Tony Cai and Linjun Zhang
Central Limit Theorem for Linear Spectral Statistics of Large Dimensional Kendall’s Rank Correlation Matrices and its Applications Zeng Li, Qinwen Wang, and Runze Li
Approximate and exact designs for total effects Xiangshun Kong, Mingao Yuan, and Wei Zheng
On Statistical Learning of Simplices: Unmixing Problem Revisited Amir Najafi, Saeed Ilchi, Amir Hossein Saberi, Abolfazl Motahari, Babak H. Khalaj, and Hamid R. Rabiee
Factor-Driven Two-Regime Regression Sokbae lee, Yuan Liao, Myung hwan Seo, Youngki Shin
Robust Bregman Clustering Claire Brécheteau, Aurélie Fischer, and Clément Levrard
LASSO-Driven Inference in Time and Space Victor Chernozhukov, Wolfgang Härdle, Chen Huang, and Weining Wang
E-values: Calibration, combination, and applications Vladimir Vovk and Ruodu Wang
Causal discovery in heavy-tailed models Nicola Gnecco, Nicolai Meinshausen, Jonas Peters, and Sebastian Engelke
SuperMix: Sparse Regularization for Mixtures Sébastien Gadat, Cathy Maugis, Clément Marteau, and Yohann De Castro
Volatility Coupling Jean Jacod, Jia Li, Zhipeng Liao
Asymptotic Distributions of High-Dimensional Distance Correlation Inference Lan Gao, Yingying Fan, Jinchi Lv, and Qi-Man Shao
Confidence intervals for multiple isotonic regression and other monotone models Hang Deng, Qiyang Han, and Cun-Hui Zhang
On Extended Admissible Procedures and their Nonstandard Bayes Risk Haosui Duanmu and Daniel M. Roy
Debiased Inverse-Variance Weighted Estimator in Two-Sample Summary-Data Mendelian Randomization Ting Ye, Jun Shao, and Hyunseung Kang
Boosted nonparametric hazards with time-dependent covariates Donald K.K. Lee, Ningyuan Chen, and Hemant Ishwaran
Universal Bayes consistency in metric spaces Steve Hanneke, Aryeh Kontorovich, Sivan Sabato, and Roi Weiss
Minimax Optimal Conditional Independence Testing Matey Neykov, Sivaraman Balakrishnan, and Larry Wasserman
Errata to “Statistical Inference for the Mean Outcome Under a Possibly Non-Unique Optimal Treatment Rule” Alex Luedtke, Aurelien Bibaut, and Mark J van der Laan
Estimation of the Number of Components of Non-parametric Multivariate Finite Mixture Models Caleb Kwon and Eric Djomo Mbakop
Robust K-means Clustering for Distributions with Two Moments Nikita Kirillovich Zhivotovskiy, Yegor Klochkov, and Alexey Kroshnin
On the rate of convergence of fully connected deep neural network regression estimates Michael Kohler and Sophie Langer
Infinite-dimensional gradient-based descent for alpha-divergence minimisation Kamélia Daudel, Randal Douc, and François Portier
Monitoring for a change point  in a sequence of distributions Lajos Horvath, Piotr Kokoszka, and Shixuan Wang
What is resolution? A statistical minimax testing perspective on super-resolution microscopy Gytis Kulaitis, Axel Munk, and Frank Werner
The distribution of the Lasso: Uniform control over sparse  balls and adaptive parameter tuning Leo Miolane and Andrea Montanari
Optimal Estimation of Change-point in Time Series Chun Yip Yau
Propriety of the reference posterior distribution in Gaussian Process modeling Joseph Muré
Optimal rates for independence testing via U-statistic permutation tests Thomas Berrett, Ioannis Kontoyiannis, and Richard John Samworth
Community Detection with Dependent Connectivity Yubai Yuan and Annie Qu
Variable Selection Consistency of Gaussian Process Regression Sheng Jiang
Optimality of Spectral Clustering in the Gaussian Mixture Model Matthias Löffler, Anderson Ye Zhang, Harrison H. Zhou
Adaptive Estimation of Multivariate Piecewise Polynomials and Bounded Variation Functions by Optimal Decision Trees Sabyasachi Chatterjee and Subhajit Goswami
Rank-based Estimation under Asymptotic Dependence and Independence, with Applications to Spatial Extremes Michaël Lalancette, Sebastian Engelke, and Stanislav Volgushev
Estimation of Smooth Functionals in Normal Models: Bias Reduction and Asymptotic Efficiency Vladimir Koltchinskii and Mayya Zhilova
Additive Regression for Non-Euclidean Responses and Predictors Jeong Min Jeon, Byeong U Park, and Ingrid Van Keilegom
Set structured global empirical risk minimizers are rate optimal in general dimensions Qiyang Han
Empirical tail copulas for functional data John H.J. Einmahl and Johan Segers
Inference for a two-stage enrichment design Zhantao Lin, Nancy Flournoy, and William Rosenberger
Existence and Uniqueness of the Kronecker Covariance MLE Mathias Drton, Satoshi Kuriki, and Peter Hoff
Willem van Zwet’s contributions to the profession Nicholas Fisher and Adrian Smith
Prediction bounds for higher order total variation regularized least squares Francesco Ortelli and Sara van de Geer
Reconciling the Gaussian and Whittle Likelihood with an application to estimation in the frequency domain Suhasini Subba Rao and Junho Yang
Stein 1956: Efficient Nonparametric Testing and Estimation A.W. van der Vaart and J.A. Wellner
Integrative Methods for Post-Selection Inference Under Convex Constraints Snigdha Panigrahi, Jonathan Taylor, and Asaf Weinstein
The Cost of Privacy: Optimal Rates of Convergence for Parameter Estimation with Differential Privacy Tony Cai, Yichen Wang, and Linjun Zhang
Adaptive Estimation of Multivariate Regression with Hidden Variables Xin Bing
Willem van Zwet’s Research Peter Bickel, Marta Fiocco, Mathisca de Gunst, and Friedrich Götze
An Optimal Statistical and Computational Framework for Generalized Tensor Estimation Rungang Han, Rebecca Willett, and Anru Zhang
Distributed Statistical Inference for Massive Data Song X Chen and Liuhua Peng
Construction of Mixed Orthogonal Arrays with High Strength Shanqi Pang, Jing Wang, Dennis K.J. Lin, and Min-qian Liu
Foundations of Structural Causal Models with Cycles and Latent Variables Stephan Bongers, Patrick Forré, Jonas Peters, and Joris Mooij
Measuring dependence in the Wasserstein distance for Bayesian nonparametric models Marta Catalano, Antonio Lijoi, and Igor Pruenster
Bridging Convex and Nonconvex Optimization in Robust PCA: Noise, Outliers, and Missing Data Yuxin Chen, Jianqing Fan, Cong Ma, and Yuling Yan
Spatial dependence and space-time trend in extreme events John Einmahl, Ana Ferreira, Laurens de Haan, Claudia Neves, and Chen Zhou
Efficiency of delayed-acceptance random walk Metropolis algorithms Chris Sherlock, Alexandre H Thiery, and Andrew Golightly
Willem van Zwet, teacher and thesis advisor Sara van de Geer and Chris A.J. Klaassen
Semiparametric Optimal Estimation with Nonignorable Nonresponse Data Kosuke Morikawa and Jae Kwang Kim
Two-Level Parallel Flats Designs Chunyan Wang and Robert W Mee
Estimating the number of components in finite mixture models via the group-sort-fuse procedure Tudor Manole and Abbas Khalili
A simple measure of conditional dependence Mona Azadkia and Sourav Chatterjee
Heteroskedastic PCA: Algorithm, Optimality, and Applications Anru Zhang, T. Tony Cai, and Yihong Wu
Charles Stein and Invariance: Beginning with  the Hunt-Stein Theorem Morris L. Eaton and Edward I. George
Online Inference with Multi-Modal Likelihood Functions Mathieu Gerber and Kari Heine
On Fixed-Domain Asymptotics, Parameter Estimation and Isotropic Gaussian Random Fields with Matern Covariance Functions Wei-Liem Loh, Saifei Sun, and Jun Wen
Adaptive Learning Rates for Support Vector Machines Working on Data with Low Intrinsic Dimension Thomas Hamm and Ingo Steinwart
Community Detection on Mixture Multi-Layer Networks Via Regularized Tensor Decomposition Bing-Yi Jing, Ting Li, Zhongyuan Lyu, and Dong Xia
Augmented Minimax Linear Estimation David Abraham Hirshberg and Stefan Wager
Wilks’ Theorem for Semiparametric Regressions with Weakly Dependent Data Marie du Roy de Chaumaray, Matthieu Marbac, and Valentin Patilea