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Papers to Appear in Subsequent Issues

Fully Adaptive Density-Based Clustering

Ingo Steinwart

Minimax Estimation in Sparse Canonical Correlation Analysis

Harrison Zhou, Chao Gao, Zongming Ma, and Zhao Ren

Computing exact D-optimal designs by mixed integer second order cone programming

Guillaume Sagnol and Radoslav Harman

Globally adaptive quantile regression with ultra-high dimensional data

Qi Zheng, Limin Peng, and Xuming He

On adaptive posterior concentration rates

Marc Hoffmann, Judith Rousseau, and Johannes Schmidt-Hieber

Functional Additive Regression

Yingying Fan, Gareth James, and Peter Radchenko

Minimax estimation of linear and quadratic functionals on sparsity classes

Olivier Collier, Laetitia Comminges, and Alexandre Tsybakov

Mimicking counterfactual outcomes to estimate causal effects

Judith Jacqueline Lok

Likelihood-based model selection for stochastic block modelsLikelihood-based model selection for stochastic block models

Y.X. Rachel Wang and Peter J Bickel

Multiple Testing of Local Maxima for Detection of Peaks in Random Fields

Dan Cheng and Armin Schwartzman

A Rate Optimal Procedure for Recovering Sparse Differences between High-Dimensional Means under Dependence

Jun Li and Ping-Shou Zhong

Online estimation of the geometric median in Hilbert spaces : non asymptotic confidence balls

Hervé Cardot, Peggy Cénac, Antoine Godichon-Baggioni

Confidence Intervals for High-Dimensional Linear Regression: Minimax Rates and Adaptivity

Tony Cai and Zijian Guo

Estimating the effect of joint interventions from observational data in sparse high-dimensional settings

Preetam Nandy, Marloes H. Maathuis, and Thomas S. Richardson

Identifiability of restricted latent class models with binary responses

Gongjun Xu

A Bernstein-type Inequality for Some Mixing Processes and Dynamical Systems with an Application to Learning

Ingo Steinwart and Hanyuan Hang

Consistency of likelihood estimation for Gibbs point processes

Frédéric Lavancier and David Dereudre

Tests for high dimensional data based on means, spatial signs and spatial ranks

Anirvan Chakraborty and Probal Chaudhuri

Inference on the mode of weak directional signals: A Le Cam perspective on hypothesis testing near singularities

Davy Paindaveine and Thomas Verdebout

Asymptotic behaviour of the empirical Bayes posteriors associated to maximum marginal likelihood estimator

Botond Szabo and Judith Rousseau

Support Consistency of Direct Sparse-Change Learning in Markov Networks

Song Liu, Suzuki Taiji, Raissa Relator, Jun Sese, Masashi Sugiyama, and Kenji Fukumizu

Statistical consistency and asymptotic normality for high-dimensional robust M-estimators

Po-Ling Loh

Randomized sketches for kernels: Fast and optimal non-parametric regression

Yun Yang, Mert Pilanci, and Martin J. Wainwright

Testing uniformity on high-dimensional spheres against monotone rotationally symmetric alternatives

Christine Cutting, Davy Paindaveine, and Thomas Verdebout

Interaction pursuit in high-dimensional multi-response regression via distance correlation

Yinfei Kong, Daoji Li, Yingying Fan, and Jinchi Lv

Nonlinear Sufficient Dimension Reduction for Functional Data

Bing Li and Jun Song

Network Vector Autoregression

Xuening Zhu, Rui Pan, Guodong Li, Yuewen Liu, and Hansheng Wang

On coverage and local radial rates of credible sets

Eduard Belitser

Total positivity in Markov structures

Shaun Fallat, Steffen Lauritzen, Kayvan Sadeghi, Caroline Uhler, Nanny Wermuth, and Piotr Zwiernik

On the Optimality of Bayesian Change-Point Detection

Dong Han, Fugee Tsung, and Jinguo Xian

Tests for Covariance Structures with High-dimensional Repeated Measurements

Ping-Shou Zhong, Wei Lan, Peter X.K. Song, and Chih-Ling Tsai

Weak Signal Identification and Inference in Model Selection

Peibei Shi and Annie Qu

A Likelihood Ratio Framework for High Dimensional Semiparametric Regression

Yang Ning, Tianqi Zhao, and Han Liu

Semimartingale detection and goodness-of-fit tests

Adam David Bull

Testing for Time-Varying Jump Activity for Pure Jump Semimartingales

Viktor Todorov

Operational time and in-sample density forecasting

Young Kyung Lee, Enno Mammen, Jens P. Nielsen, and Byeong U. Park

Asymptotics of Empirical Eigen-structure for High Dimensional Spiked Covariance

Weichen Wang and Jianqing Fan

Computational and Statistical Boundaries for Submatrix Localization in a Large Noisy Matrix

T. Tony Cai, Tengyuan Liang, and Alexander Rakhlin

Tests for separability in nonparametric covariance operators of random surfaces

John Aston, Davide Pigoli, and Shahin Tavakoli

Identification of universally optimal circular designs for the interference model

Wei Zheng, Mingyao Ai, and Kang Li

Co-clustering of Nonsmooth Graphons

David Choi

Minimax theory of estimation of linear functionals of the deconvolution density with or without sparsity

Marianna Pensky

Nonparametric change-point analysis of volatility

Markus Bibinger, Moritz Jirak, and Mathias Vetter

A new approach to optimal designs for correlated observations

Holger Dette, Maria Konstantinou, and Anatoly Zhigljavsky

Rare-event Analysis for Extremal Eigenvalues of white Wishart matrices

Tiefeng Jiang, Kevin Leder, and Gongjun Xu

Robust Discrimination Designs over Hellinger Neighbourhoods

Rui Hu and Douglas P. Wiens

Nonparametric Bayesian Posterior Contraction Rates for Discretely Observed Scalar Diffusions

Richard Nickl and Jakob Soehl

A New Perspective on Boosting in Linear Regression via Subgradient Optimization and Relatives

Rahul Mazumder, Robert Freund, and Paul Grigas

Asymptotic and finite-sample properties of estimators based on stochastic gradients

Panos Toulis and Edoardo M. Airoldi

Functional central limit theorems for single-stage sampling designs

Hélène Boistard, Hendrik P. Lopuhaä, and Anne Ruiz-Gazen

Asymptotic Normality of Scrambled Geometric Net Quadrature

Kinjal Basu and Rajarshi Mukherjee

Yule's "Nonsense Correlation" Solved!

Philip Andrew Ernst, Larry Alan Shepp, and Abraham J. Wyner

'Local' vs. 'global' parameters -- breaking the gaussian complexity barrier

Shahar Mendelson

Confounder Adjustment in Multiple Hypothesis Testing

Jingshu Wang, Qingyuan Zhao, Trevor Hastie, and Art B. Owen

Gaussian Approximation for High Dimensional Time Series

Danna Zhang and Wei Biao Wu

Detection and Feature Selection in Sparse Mixture Models

Nicolas Verzelen and Ery Arias-Castro

Sharp detection in PCA under correlations: all eigenvalues matter

Edgar Dobriban

Minimax Estimation of a Functional on a Structured High-Dimensional Model

James M. Robins, Lingling Li, Rajarshi Mukherjee, Eric Tchetgen Tchetgen, and Aad van der Vaart

Asymptotic theory of generalized estimating equations based on jack-knife pseudo-observations

Morten Overgaard, Erik Thorlund Parner, and Jan Pedersen

Bayesian Poisson Calculus for Latent Feature Modeling via Generalized Indian Buffet Process Priors

Lancelot Fitzgerald James

Information-regret compromise in covariate-adaptive treatment allocation

Asya Metelkina and Luc Pronzato

Sparse CCA: Adaptive Estimation and Computational Barriers

Chao Gao, Zongming Ma, and Harrison Zhou

Optimal designs for dose response curves with common parameters

Chrystel Feller, Kirsten Schorning, Holger Dette, Georgina Bermann, and Björn Bornkamp

False Discoveries occur Early on the Lasso Path

Weijie Su, Malgorzata Bogdan, and Emmanuel Candes

Phase transitions for high dimensional clustering and related problems

Jiashun Jin, Zheng Tracy Ke, and Wanjie Wang

Bayesian Detection of Image Boundaries

Meng Li and Subhashis Ghosal

On the validity of resampling methods under long memory

Murad S. Taqqu and Shuyang Bai

Spectrum Estimation from Samples

Weihao Kong and Gregory Valiant

On the contraction properties of some high-dimensional quasi-posterior distributions

Yves F Atchadé

CoCoLasso for High-dimensional Error-in-variables Regression

Abhirup Datta and Hui Zou

Nonasymptotic Analysis of Semiparametric Regression Models with High-Dimensional Parametric Coefficients

Ying Zhu

Consistent Parameter Estimation for LASSO and Approximate Message Passing

Ali Mousavi, Arian Maleki, and Richard G. Baraniuk

Support recovery without incoherence: A case for nonconvex regularization

Po-Ling Loh and Martin Wainwright

Optimal Design of fMRI Experiments Using Circulant (Almost-)Orthogonal Arrays

Yuan-Lung Lin, Frederick Kin Hing Phoa, and Ming-Hung Kao

Chernoff Index for Cox Test of Separate Parametric Families

Xiaoou Li, Jingchen Liu, and Zhiliang Ying

Adaptive Bernstein-von Mises theorems in Gaussian white noise

Kolyan Ray

Targeted sequential design for targeted learning inference of the optimal treatment rule and its mean reward

Antoine Chambaz, Wenjing Zheng, and Mark J van der Laan

Nonparametric goodness-of-fit tests for uniform stochastic ordering

Chuan-Fa Tang, Dewei Wang, and Joshua M Tebbs

Selecting the Number of Principal Components: Estimation of the True Rank of a Noisy Matrix

Yunjin Choi, Jonathan Taylor, and Robert Tibshirani

Extended Conditional Independence and Applications in Causal Inference

Panayiota Constantinou and A. Philip Dawid

A weight-relaxed model averaging approach for exploiting high dimensionality, weak signals and model misspecification in generalized linear models

Tomohiro Ando and Ker-chau Li

Structural similarity and difference testing on multiple sparse Gaussian graphical models

Liu Weidong

Optimal bounds for aggregation of affine estimators

Pierre C. Bellec

Rate-Optimal Perturbation Bounds for Singular Subspaces with Applications to High-Dimensional Statistics

T. Tony Cai and Anru Zhang

Estimating a probability mass function with unknown labels

Dragi Anevski, Richard D. Gill, and Stefan Zohren

Exact formulas for the normalizing constants of Wishart distributions for graphical models

Caroline Uhler, Alex Lenkoski, and Donald Richards

Consistent Parameter Estimation for LASSO and Approximate Message Passing

Ali Mousavi, Arian Maleki, and Richard G. Baraniuk

On semidefinite relaxations for the block model

Arash Ali Amini and Elizaveta Levina

Optimal Sequential Detection in Multi-Stream Data

Hock Peng Chan

A General Theory of Pathwise Coordinate Optimization

Tuo Zhao, Han Liu, and Tong Zhang

Conditional Mean and Quantile Dependence Testing in High Dimension

Xianyang Zhang, Shun Yao, and Xiaofeng Shao

High-Dimensional Asymptotics of Prediction: Ridge Regression and Classification

Edgar Dobriban and Stefan Wager

Testing independence in high dimensions with sums of squares of rank correlations

Dennis Leung and Mathias Drton

High dimensional censored quantile regression

Qi Zheng, Limin Peng, and Xuming He

Local M-estimation with Discontinuous Criterion for Dependent and Limited Observations

Myung hwan Seo and Taisuke Otsu

Mixture Inner Product Spaces and Their Application to Functional Data Analysis

Zhenhua Lin, Hans-Georg Müller, and Fang Yao

Bayesian Estimation of Sparse Signals with a Continuous Spike-and-Slab Prior

Veronika Rockova

Strong orthogonal arrays of strength two plus

Yuanzhen He, Ching-Shui Cheng, and Boxin Tang
Statistical inference for spatial statistics defined in the Fourier domain Suhasini Subba Rao

On the asymptotic theory of new bootstrap confidence bounds

Charl Pretorius and Jan Willem Hendrik Swanepoel

On the inference about the spectral distribution of high-dimensional covariance matrix based on high-frequency noisy observations

Ningning Xia and Xinghua Zheng

Online Rules for Control of False Discovery Rate and False Discovery Exceedance

Adel Javanmard and Andrea Montanari

Frequency Domain Minimum Distance Inference for Possibly Noninvertible and Noncausal ARMA models

Carlos Velasco and Ignacio N. Lobato

On consistency and sparsity for sliced inverse regression in high dimensions

Qian Lin, Zhigen Zhao, and Jun S. Liu

Regularization and the small-ball method I: sparse recovery

Guillaume Lecue and Shahar Mendelson

Gaussian and bootstrap approximations for high-dimensional U-statistics and their applications

Xiaohui Chen

Selective inference with a randomized response

Xiaoying Tian and Jonathan Taylor

Multiscale Blind Source Separation

Merle Behr, Chris Holmes, and Axel Munk

Sharp oracle inequalities for Least Squares estimators in shape restricted regression

Pierre C. Bellec

Oracle Inequalities for Sparse Additive Quantile Regression in Reproducing Kernel Hilbert Space

Shaogao Lv, Huazhen Lin, Heng Lian, and Jian Huang

I-LAMM: Simultaneous Control of Algorithmic Complexity and Statistical Error

Jianqing Fan, Han Liu, Qiang Sun, and Tong Zhang

On Bayesian index policies for sequential resource allocation

Emilie Kaufmann

High-Dimensional A-Learning for Optimal Dynamic Treatment Regimes

Chengchun Shi, Ailin Fan, Rui Song, and Wenbin Lu

Testing independence with high-dimensional correlated samples

Xi Chen and Weidong Liu

Variable selection with Hamming loss

Cristina Butucea, Natalia A. Stepanova, and Alexandre B. Tsybakov

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