Papers to Appear in Subsequent Issues

On the optimality of sliced inverse regression in high dimensions Qian Lin, Jun S Liu, Dongming Huang, and Xinran Li
Asymptotic Optimality in Stochastic Optimization John Duchi and Feng Ruan
Minimax  Estimation of Large Precision Matrices with Bandable Cholesky Factor Yu Liu and Zhao Ren
Concordance and Value Information Criteria for Optimal Treatment Decision Chengchun Shi, Rui Song, and Wenbin Lu
Testability of High-Dimensional Linear Models with Non-Sparse Structures Jelena Bradic, Jianqing Fan, and Yinchu Zhu
Statistically Optimal and Computationally Efficient Low Rank Tensor Completion from Noisy Entries Dong Xia, Ming Yuan, and Cunhui Zhang
Transfer Learning for Nonparametric Classification: Minimax Rate and Adaptive Classifier Tony Cai and Hongji Wei
Adaptation in Multivariate Log-Concave Density Estimation Oliver Y. Feng, Adityanand Guntuboyina, Arlene K. H. Kim, and Richard J. Samworth
Asymptotically Independent U-Statistics in High-Dimensional Testing Yinqiu He, Gongjun Xu, Chong Wu, and Wei Pan
Frequentist validity of Bayesian limits Bas Kleijn
Optimal Change Point Detection and Localization in Sparse Dynamic Networks Daren Wang, Yi Yu, and Alessandro Rinaldo
Convergence of covariance and spectral density estimates for high dimensional locally stationary processes Danna Zhang and Wei Biao Wu
Wordlength Enumerator for Fractional Factorial Designs Yu Tang and Hongquan Xu
Estimating minimum effect with outlier selection Alexandra Carpentier, Sylvain Delattre, Etienne Roquain, and Nicolas Verzelen
Multiple block sizes and overlapping blocks for multivariate time series extremes Nan Zou, Stanislav Volgushev, and Axel Bücher
Estimation of Low-Rank Matrices via Approximate Message Passing Andrea Montanari and Ramji Venkataramanan
Asymptotics for Spherical Functional Autoregressions Alessia Caponera and Domenico Marinucci
Singular vector and singular subspace distribution for the matrix denoising model Zhigang Bao, Xiucai Ding, and Ke Wang
Robust multivariate mean estimation: the optimality of trimmed mean Gabor Lugosi and Shahar Mendelson
Classification accuracy as a proxy for two-sample testing Ilmun Kim, Aaditya Ramdas, Aarti Singh, and Larry Wasserman
Asymmetry Helps: Eigenvalue and Eigenvector Analyses of Asymmetrically Perturbed Low-Rank Matrices Yuxin Chen, Chen Cheng, and Jianqing Fan
Complex sampling designs:  uniform limit theorems and applications Jon A. Wellner and Qiyang Han
Predictive inference with the jackknife+ Rina Foygel Barber, Emmanuel J Candes, Aaditya Ramdas, and Ryan J Tibshirani
Robust Estimation of Superhedging Prices Jan Krzysztof Obloj and Johannes Wiesel
Concentration of kernel matrices with application to kernel spectral clustering Arash Ali Amini and Zahra Sadat Razaee
A Rule of Thumb: Run Lengths to False Alarm of Many Types of Control Charts Run in Parallel on Dependent Streams Are Asymptotically Independent Moshe Pollak
Density deconvolution under general assumptions  on the distribution  of   measurement errors Alexander Goldenshluger and Denis Belomestny
Sharp Minimax Distribution Estimation for Current Status Censoring With or Without Missing Sam Efromovich
Wasserstein F-tests and confidence bands for the Fréchet regression of density response curves Alexander Petersen, Xi Liu, and Afshin A. Divani
High-dimensional nonparametric density estimation via symmetry and shape constraints Min Xu and Richard J. Samworth
Average treatment effects in the presence of unknown interference Fredrik Sävje, Peter M. Aronow, and Michael G. Hudgens
The adaptive Wynn-algorithm in generalized linear models with univariate response Norbert Gaffke, Fritjof Freise, and Rainer Schwabe
Optimal disclosure risk assessment Stefano Favaro, Federico Camerlenghi, Zacharie Naulet, and Francesca Panero
Network Representation Using Graph Root Distributions Jing Lei
Multivariate extensions of isotonic regression and total variation denoising via entire monotonicity and Hardy-Krause variation Billy Fang, Adityanand Guntuboyina, and Bodhisattva Sen
Analysis of “Learn-As-You-Go”‘ (LAGO) Studies Daniel Nevo, Judith J. Lok, and Donna Spiegelman
Necessary and Su cient conditions for variable selection consistency of the LASSO in high dimensions Soumendra N Lahiri
A shrinkage principle for heavy-tailed data: high-dimensional robust low-rank matrix recovery Jianqing Fan, Weichen Wang, and Ziwei Zhu
Empirical Process Results for Exchangeable Arrays Xavier D’Haultfoeuille, Laurent Davezies, and Yannick Guyonvarch
Survival analysis via hierarchically dependent mixture hazards Federico Camerlenghi, Antonio Lijoi, and Igor Pruenster
Adaptive importance sampling via kernel smoothing Bernard Delyon and François Portier
Distributed linear regression by averaging Edgar Dobriban and Yue Sheng
Consistent Nonparametric Estimation  for Heavy-tailed Sparse Graphs Christian Borgs, Jennifer T. Chayes, Henry Cohn, and Shirshendu Ganguly
Subspace Estimation from Unbalanced and Incomplete Data Matrices: ℓ2,∞ Statistical Guarantees Changxiao Cai, Gen Li, Yuejie Chi, Vincent Poor, and Yuxin Chen
Asymptotic distribution  and convergence rates of stochastic algorithms for entropic optimal transportation between probability measures Jérémie Bigot and Bernard Bercu
Multiscale geometric feature extraction for high-dimensional and non-Euclidean data with applications Gabriel Chandler and Wolfgang Polonik
Coverage of Credible Intervals in Nonparametric Monotone Regression Moumita Chakraborty and Subhashis Ghosal
Linearized two-layers neural networks in high dimension Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, and Andrea Montanari
Time-uniform, nonparametric, nonasymptotic confidence sequences Steven R Howard, Aaditya Ramdas, Jon McAuliffe, and Jasjeet Sekhon
Strong Selection Consistency of Bayesian Vector Autoregressive Models based on a Pseudo-Likelihood Approach George Michailidis
Correction note: “Optimal two-stage procedures for estimating location and size of the maximum of a multivariate regression function” Eduard Belitser, Subhashis Ghosal, and Harry van Zanten
Minimax rates in sparse, high-dimensional changepoint detection Haoyang Liu, Chao Gao, and Richard J Samworth
Spiked separable covariance matrices and principal components Xiucai Ding and Fan Yang
Center-Outward Distribution/Quantile Functions, Ranks, and Signs in R^d: a Measure Transportation Approach Marc Hallin, Estasio del Barrio, Juan Cuesta Albertos, and Carlos Matran
Minimax estimation of smooth optimal transport maps Jan-Christian Hütter and Philippe Rigollet
Estimation and Inference in the Presence of Fractional d = 1/2 and Weakly Nonstationary Processes James Duffy and Ioannis Kasparis
Only Closed Testing Procedures are Admissible for Controlling False Discovery Proportions Jelle Goeman, Jesse Hemerik, and Aldo Solari
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
Erratum: Higher Order Elicitability and Osband’s Principle Tobias Fissler and Johanna F. Ziegel
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 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