Papers to Appear in Subsequent Issues

Community Detection on Mixture Multi-Layer Networks Via Regularized Tensor Decomposition Bing-Yi Jing, Ting Li, Zhongyuan Lyu, and Dong Xia
Continuous-time targeted minimum loss-based estimation of intervention-specific mean outcomes Helene Charlotte Rytgaard, Thomas A. Gerds, and Mark J. van der Laan
Convergence of de Finetti’s mixing measure in latent structure models for observed exchangeable sequences Yun Wei and XuanLong Nguyen
Correction to: Asymptotic genealogies of interacting particle systems with an application to sequential Monte Carlo Jere Koskela, Paul A Jenkins, Adam M Johansen, and Dario Spano
Conditional Calibration for False Discovery Rate Control Under Dependence William Fithian and Lihua Lei
Detecting Multiple Replicating Signals using Adaptive Filtering Procedures Jingshu Wang, Lin Gui, Weijie J. Su, Chiara Sabatti, and Art B. Owen
Design admissibility and de la Garza phenomenon in multi-factor experiments Holger Dette, Xin Liu, and Rong-Xian Yue
Evidence Factors from Multiple, Possibly Invalid, Instrumental Variables Anqi Zhao, Youjin Lee, Dylan S Small, and Bikram Karmakar
Consistent Order Selection for ARFIMA Processes Hsueh-Han Huang, Ngai Hang Chan, Kun Chen, and Ching-Kang Ing
Optimal signal detection in some spiked random matrix models: likelihood ratio tests and linear spectral statistics Debapratim Banerjee and Zongming Ma
On universally consistent and fully distribution-free rank tests of vector independence Hongjian Shi, Marc Hallin, Mathias Drton, and Fang Han
Doubly Debiased Lasso: High-Dimensional Inference under Hidden Confounding Zijian Guo, Domagoj Ćevid, and Peter Bühlmann
Deep Learning for the Partially Linear Cox Model Qixian Zhong, Jonas Mueller, and Jane-Ling Wang
A General Framework: Optimal Difference-based Variance Estimator in Time Series Kin Wai Chan
Grouped Variable Selection with Discrete Optimization: Computational and Statistical Perspectives Hussein Hazimeh, Rahul Mazumder, and Peter Radchenko
Ridge Regression Revisited: Debiasing, Thresholding and Bootstrap Yunyi Zhang and Dimitris Politis
Cube Root Weak Convergence of Empirical Estimators of a Density Level Set Philippe Berthet and John H.J. Einmahl
Two-sample Testing for High-Dimensional Multinomials under Rare/Weak Perturbations David Leigh Donoho and Alon Kipnis
A New and Flexible Design Construction for Orthogonal Arrays and their Generalizations for Modern Applications Yuanzhen He, Chunfang Devon Lin, and Fasheng Sun
Consistency of Bayesian inference for multivariate max-stable distributions Simone Andrea Padoan and Stefano Rizzelli
Minimax estimation of smooth densities in Wasserstein distance Jonathan Niles-Weed and Quentin Berthet
Large scale inference with block structure Guenther Walther and Jiyao Kou
Uniform convergence of local Fréchet regression, with applications to locating extrema and time warping for metric space valued trajectories Yaqing Chen and Hans-Georg Müller
Cointegration in large VARs Anna Bykhovskaya and Vadim Gorin
Limit theorems for invariant distributions Morgane Austern and Peter Orbanz
Partial Recovery for Top-k Ranking: Optimality of MLE and Sub-Optimality of Spectral Method Pinhan Chen, Chao Gao, and Anderson Zhang
Distributed Adaptive Gaussian Mean Estimation with Unknown Variance: Interactive Protocol Helps Adaptation Tony Cai and Hongji Wei
A data-adaptive method for estimating density level sets under shape conditions Alberto Rodríguez-Casal and Paula Saavedra-Nieves
Asymptotic accuracy of the saddlepoint approximation for maximum likelihood estimation Jesse Goodman
A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum-1-Norm Interpolated Classifiers Tengyuan Liang and Pragya Sur
Generalization Error Bounds of Dynamic Treatment Regimes in Regression-Based Learning Eun Jeong Oh, Min Qian, and Ken Cheung
Intrinsic Riemannian Functional Data Analysis for Sparse Longitudinal Observations Lingxuan Shao, Zhenhua Lin, and Fang Yao
Strong mixing condition for Hawkes processes and application to Whittle estimation from count data Felix Cheysson and Gabriel Lang
Model selection in the space of Gaussian models invariant by symmetry Piotr Graczyk, Hideyuki Ishi, Bartosz Kołodziejek, and Helene Massam
Optimal Full Ranking from Pairwise Comparisons Pinhan Chen, Chao Gao, and Anderson Ye Zhang
A CLT for second difference estimators with an application to volatility and intensity Emil Aas Stoltenberg, Per Aslak Mykland, and Lan Zhang
Confidence Regions Near Singular Information and Boundary Points With Applications to Mixed Models Karl Oskar Ekvall and Matteo Bottai
Sharp optimal recovery in the two Component Gaussian Mixture Model Mohamed Ndaoud
Computational Barriers to Estimation from Low-Degree Polynomials Tselil Schramm and Alexander S. Wein
Precise Statistical Analysis of Classification Accuracies for Adversarial Training Adel Javanmard and Mahdi Soltanolkotabi
Exact minimax risk for linear least squares, and the lower tail of sample covariance matrices Jaouad Mourtada
Stochastic Continuum-armed Bandits with Additive Models: Minimax Regrets and Adaptive Algorithm Tony Cai and Hongming Pu
Asymptotic independence of spiked eigenvalues and linear spectral statistics for large sample covariance matrices Zhixiang Zhang, Shurong Zheng, Guangming Pan, and Pingshou Zhong
Central Limit Theorem and Bootstrap Approximation in High Dimensions: Near 1/√n R Rates via Implicit Smoothing Miles Lopes
Learning Mixtures of Permutations: Groups of Pairwise Comparisons and Combinatorial Method of Moments Cheng Mao and Yihong Wu
Generalized resilience and robust statistics Banghua Zhu, Jiantao Jiao, and Jacob Noah Steinhardt
Testing Goodness-of-Fit and Conditional Independence with Approximate Co-Sufficient Sampling Rina Foygel Barber and Lucas Janson
Nonregular and Minimax Estimation of Individualized Thresholds in High Dimension with Binary Responses Huijie Feng, Yang Ning, and Jiwei Zhao
A No-Free-Lunch Theorem for Multitask Learning Steve Hanneke and Samory Kpotufe
On the robustness of minimum-norm interpolators Geoffrey Chinot, Matthias Löffler, and Sara van de Geer
Random Graph Asymptotics for Treatment Effect Estimation under Network Interference Shuangning Li and Stefan Wager
New Edgeworth-type expansions with finite sample guarantees Mayya Zhilova
Improved Central Limit Theorem and Bootstrap Approximations in High Dimensions Victor Chernozhukov, Denis Chetverikov, Kengo Kato, and Yuta Koike
Characterizing the SLOPE Trade-off: A Variational Perspective and the Donoho-Tanner Limit Zhiqi Bu, Jason Matthew Klusowski, Cynthia Rush, and Weijie J Su
Bounds on the Conditional and Average Treatment Effect with Unobserved Confounding Factors Steve Yadlowsky, Hongseok Namkoong, Sanjay Basu, John C Duchi, and Lu Tian
An ℓp theory of PCA and spectral clustering Emmanuel Abbe, Jianqing Fan, and Kaizheng Wang
Estimation of Smooth Functionals in High-Dimensional Models: Bootstrap Chains and Gaussian Approximation Vladimir Koltchinskii
A minimax framework for quantifying risk-fairness trade-off in regression Evgenii Chzhen and Nicolas Schreuder
Affine-equivariant inference for multivariate location under Lp loss functions Alexander Dürre and Davy Paindaveine
Consistency of invariance-based randomization tests Edgar Dobriban
Is infinity that far? A  Bayesian nonparametric perspective of finite mixture models Raffaele Argiento and Maria De Iorio
Global and Individualized Community Detection in Inhomogeneous Multilayer Networks Shuxiao Chen, Sifan Liu, and Zongming Ma
Inference of Synchrosqueezing Transform – Toward a Unified Statistical Analysis of Nonlinear-Type Time-Frequency Analysis Hau-tieng Wu, Matt Sourisseau, and Zhou Zhou