Papers to Appear in Subsequent Issues

Matching Methods for Observational Studies Derived from Large Administrative Databases Ruoqi Yu, Jeffrey H Silber, and Paul R Rosenbaum
Sparse regression: Scalable algorithms and empirical performance Dimitris Bertsimas, Jean Pauphilet, and Bart Van Parys
Convex Relaxation Methods for Community Detection Xiaodong Li, Yudong Chen, and Jiaming Xu
Linear mixed models under endogeneity: modeling sequential treatment effects with application to a mobile health study Tianchen Qian, Predrag Klasnja, and Susan A. Murphy
Invariance, Causality and Robustness Peter Bühlmann
Outcome-wide longitudinal designs for causal inference: a new template for empirical studies Tyler J VanderWeele, Maya B Mathur, and Ying Chen
Best Subset, Forward Stepwise, or Lasso? Analysis and Recommendations Based on Extensive Comparisons Trevor hastie, Robert Tibshirani, and Ryan Tibshirani
A nonparametric super-efficient estimator of the average treatment effect David Benkeser, Wilson Cai, and Mark J van der Laan
Minimax Rates in Network Analysis: Graphon Estimation, Community Detection and Hypothesis Testing Chao Gao and Zongming Ma
Computational considerations when matching in large samples Fredrik Sävje
Comment Mark M Fredrickson, Josh Errickson, and Ben B Hansen
Commentary on Yu et al.:  Opportunities and Challenges for Matching Methods in Large Databases Elizabeth A Stuart and Benjamin Ackerman
Exponential-Family Models of Random Graphs: Inference in Finite-, Super-, and Infinite-Population Scenarios Michael Schweinberger, Pavel N. Krivitsky, Carter T. Butts, and Jonathan Stewart
Bipartite Causal Inference with Interference Corwin M Zigler and Georgia Papadogeorgou
A general framework for Vecchia approximations of Gaussian processes Matthias Katzfuss and Joseph Guinness
Additive and multiplicative effects network models Peter Hoff
A Unified Primal Dual Active Set Algorithm for Nonconvex Sparse Recovery Jian Huang, Yuling Jiao, Bangti Jin, Jin Liu, Xiliang Lu, and Can Yang
A statistical framework for modern network science Harry Crane and Walter Dempsey
Judicious Judgment Meets Unsettling Updating: Dilation, Sure Loss, and Simpson’s Paradox Ruobin Gong and Xiao-Li Meng
A Conversation with J. Stuart (Stu) Hunter Richard D. De Veaux
On general notions of depth for regression Yijun Zuo
Comment on: Invariance, Causality and Robustness by P.Buhlmann Vanessa Didelez
Discussion of ‘Outcome-wide longitudinal designs for causal inference’ Stijn Vansteelandt
Comment on “A nonparametric super-efficient estimator of the average treatment effect” by Benkeser, Cai, and van der Laan Mireille E Schnitzer
Outcome-wide individualized treatment strategies Ashkan Ertefaie and Brent A Johnson
Invariance and Causal Inference: Comment on a Paper by Buhlmann Stefan Wager
Automated analyses: Because we can, does it mean we should? Susan M Shortreed and Erica E.M. Moodie
Comment: A nonparametric super-efficient estimator of the average treatment effect Fan Li
A Conversation with Tze Leung Lai Zhiliang Ying, Dylan S. Small, and Ying Lu
A new template for empirical studies: from positivity to Positivity Rhian Mair Daniel
Clarifying endogeneous data structures and consequent modelling choices using causal graphs Erica E M Moodie and David A Stephens
The Box-Cox Transformation: Review and Extensions Anthony C Atkinson, Marco Riani, and Aldo Corbellini
Non-commutative Probability and Multiplicative Cascades Ian Wray McKeague
Discussion of “Linear mixed models with endogenous covariates: modeling sequential treatment effects with application to a mobile health study” Kristin A Linn
Comment Hunyong Cho, Joshua Paul Zitovsky, Xinyi Li, Minxin Lu, Kushal Shah, John Sperger, Matthew C. B. Tsilimigras, and Michael Rene Kosorok
A selective overview of deep learning Jianqing Fan, Cong Ma, and Yiqiao Zhong
Stochastic Approximation: from Statistical Origin to Big-Data, Multidisciplinary Applications Tze Leung Lai and Hongsong Yuan
Robust high dimensional factor models with applications to statistical machine learning Jianqing Fan, Kaizheng Wang, Yiqiao Zhong, and Ziwei Zhu
On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning Lin Liu, Rajarshi Mukherjee, and James Matthew Robins
On estimation and inference in latent structure random graphs Avanti Athreya, Minh Tang, Youngser Park, and Carey E. Priebe
A Hybrid Scan Gibbs Sampler for Bayesian Models with Latent Variables Grant Backlund, James P Hobert, Yeun Ji Jung, and Kshitij Khare
Response to comments on “A nonparametric super-efficient estimator of the average treatment effect” David Benkeser, Weixin Cai, and Mark J van der Laan
Reply: Matching Methods for Observational Studies Derived from Large Administrative Databases Ruoqi Yu, Jeffrey H Silber, and Paul R Rosenbaum
The Future of Outcome-Wide Studies Tyler J VanderWeele, Maya B Mathur, and Ying Chen
Network Modeling in Biology: Statistical Methods for Gene and Brain Networks Y.X. Rachel Wang, Lexin Li, Jingyi Jessica Li, and Haiyan Huang
Maximum likelihood multiple imputation: Faster imputations and consistent standard errors without posterior draws Paul T von Hippel and Jonathan Bartlett
Rejoinder to “Linear mixed models with endogenous covariates: modeling sequential treatment effects with application to a mobile health study” Tianchen Qian, Predrag Klasnja, and Susan A. Murphy
A horse race between the block maxima method and the peak-over-threshold approach Axel Buecher and Chen Zhou
Discussion of “On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning” Edward H Kennedy, Sivaraman Balakrishnan, and Larry A Wasserman
Rejoinder: Invariance, Causality and Robustness (2018 Neyman Lecture) Peter Bühlmann
Khinchin’s 1929 paper on von Mises’s frequency theory of probability Lukas M. Verburgt
Random-Matrix Theory and Its Applications Alan Julian Izenman