Papers to Appear in Subsequent Issues

Models as Approximations, Part I: A Conspiracy of Random Regressors and Model Deviations Against Classical Inference in Regression Andreas Buja, Richard Berk, Lawrence Brown, Edward George, Emil Pitkin, Mikhail Traskin, Linda Zhao, and Kai Zhang
Models as Approximations — Part II: A General Theory of Model-Robust Regression Andreas Buja, Richard Berk, Lawrence Brown, Ed George, Arun Kumar Kuchibhotla, and Linda Zhao
Laplace’s theories of cognitive illusions, heuristics, and biases Andrew Gelman and Joshua Miller
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
Model-based approach to the joint analysis of single-cell data on chromatin accessibility and gene expression Zhixiang Lin, Mahdi Zamanighomi, Timothy Daley, Shining Ma, and Wing Hung Wong
Convex Relaxation Methods for Community Detection Xiaodong Li, Yudong Chen, and Jiaming Xu
Fano’s inequality for random variables Sebastien Gerchinovitz, Pierre Menard, and Gilles Stoltz
Larry Brown’s Contributions to Parametric Inference, Decision Theory and Foundations: A Survey James Berger and Anirban DasGupta
Gaussianization Machines for Non-Gaussian Function Estimation Models Tony Cai
Equitability, Interval Estimation, and Statistical Power Yakir Reshef, David Reshef, Pardis Sabeti, and Michael Mitzenmacher
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
Discussion of Models as Approximations I & II Sara Anna van de Geer
Comment on Models as Approximations, Parts I and II, by Buja et al. Jerald Lawless
Comment on “Models as Approximations, Parts I and II” Nikki L. B. Freeman, Xiaotong Jiang, Owen E. Leete, Daniel J. Luckett, Teeranan (Ben) Pokaprakarn, and Michael R. Kosorok
Discussion of Buja et al Models as approximations I and II Dag Bjarne Tjostheim
Comment on “Models as Approximations 1: Consequences Illustrated with Linear Regression” by A. Buja, R. Berk, L. Brown, E. George, E. Pitkin, L. Zhan, and K. Zhang Roderick Joseph Little
LGM split sampler: An efficient MCMC sampling scheme for latent Gaussian models Óli Páll Geirsson, Birgir Hrafnkelsson, Daniel Simpson, and Helgi Sigurdarson
Outcome-wide longitudinal designs for causal inference: a new template for empirical studies Tyler J VanderWeele, Maya B Mathur, and Ying Chen
Risk models for breast cancer and their validation Adam Robert Brentnall and Jack Cuzick
Some Statistical Issues in Climate Science Michael Stein
Checking for prior-data conflict using prior-to-posterior divergences David John Nott, Xueou Wang, Michael Evans, and Berthold-Georg Englert
A Generalized Approach to Power Analysis for Local Average Treatment Effects Kirk Bansak
Best Subset, Forward Stepwise, or Lasso? Analysis and Recommendations Based on Extensive Comparisons Trevor hastie, Robert Tibshirani, and Ryan Tibshirani
A Conversation with Grace Wahba Douglas Nychka, Ping Ma, and Douglas Bates
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
On the probability that two random integers are coprime Jing Lei and Joseph B. Kadane
A Tale of Two Parasites: statistical modelling to support disease control programmes in Africa Peter John Diggle, Emanuele Giorgi, Julienne Atsame, Sylvie Ntsama Ella, Kisito Ogoussan, and Katherine Gass
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
Comparative Study of Differentially Private Data Synthesis Methods Claire McKay Bowen and Fang Liu
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
(Some of) Larry Brown’s work on admissibility Iain M Johnstone
Quantum Science and Quantum Technology Yazhen Wang
Discussion of the papers by Buja et al. Anthony Davison, Erwan Koch, and Jonathan Koh
Models as (deliberate) approximations David Whitney, Ali Shojaie, and Marco Carone
Statistical Inference From a Predictive Perspective Alessandro Rinaldo, Ryan Tibshirani, and Larry Wasserman
Bipartite Causal Inference with Interference Corwin M Zigler and Georgia Papadogeorgou
Laplace and Cognitive Illusions Daniel Kahneman and Maya Bar-Hillel
Comment:  Illusions, Then and Now Glenn Shafer
Statistical Methodology in Single-Molecule Experiments Chao Du and Samuel Kou
Statistical Molecule Counting in Super-Resolution Fluorescence  Microscopy: Towards Quantitative Nanoscopy Thomas Staudt, Timo Aspelmeier, Oskar Laitenberger, Claudia Geisler, Alexander Egner, and Axel Munk
Statisticians’ Views on Data Science Junhui Cai, Avishai Mandelbaum, Chaitra Nagaraja, Haipeng Shen, and Linda Zhao
A general framework for Vecchia approximations of Gaussian processes Matthias Katzfuss and Joseph Guinness
Discussion of  “Models as Approximations, Parts I & II” by Andreas Buja and coauthors Dalia Ghanem and Todd A. Kuffner
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