Papers to Appear in Subsequent Issues

Probabilistic Integration: A Role in Statistical Computation? Francois-Xavier Briol, Chris Oates, Mark Girolami, Michael Osborne, and Dino Sejdinovic
Gaussian integrals and Rice series in crossing distributions – to compute the distribution of maxima and other features of Gaussian processes Georg Lindgren
A Conversation with Piet Groeneboom Geurt Jongbloed
Automated  versus  do-it-yourself methods  for  causal  inference: Lessons  learned  from  a  data analysis  competition Vincent Dorie, Jennifer Hill, Uri Shalit, Marc Scott, and Dan Cervone
Generalized Multiple Importance Sampling Víctor Elvira, Luca Martino, David Luengo, and Mónica F. Bugallo
Bayes, Oracle Bayes, and Empirical Bayes Bradley Efron
A kernel regression procedure in the 3D shape space with an application to online sales of children’s wear Gregorio Quintana-Orti and Amelia Simó
Unreasonable effectiveness of Monte Carlo Art B Owen
Comment Michael L Stein and Ying Hung
A Conversation with Dick Dudley Vladimir Koltchinskii, Richard Nickl, and Philippe Rigollet
Causal Inference Competitions: Where Should We Aim? Ehud Karavani, Tal El-Hay, Yishai Shimoni, and Chen Yanover
Will competition-winning methods for causal inference also succeed in practice? Qingyuan Zhao, Luke Keele, and Dylan Small
Statistical Analysis of Zero-inflated Non-negative Continuous Data: a Review Lei Liu, Ya-Chen Tina Shih, Robert L. Strawderman, Daowen Zhang, Bankole Johnson, and Haitao Chai
Contributions of model features to BART causal inference performance using ACIC 2016 competition data Nicole Bohme Carnegie
Rejoinder for “Probabilistic Integration: A Role in Statistical Computation?” Francois-Xavier Briol, Chris J. Oates, Mark Girolami, Michael A. Osborne, and Dino Sejdinovic
Spherical cows in a vacuum: Data analysis competitions for causal inference Miguel Hernan
Comment on “Probabilistic Integration: A Role in Statistical Computation?” Fred J Hickernell and R. Jagadeeswaran
The Importance of Being Clustered: Uncluttering the Trends of Statistics from 1970 to 2015 Laura Anderlucci, Angela Montanari, and Cinzia Viroli
Producing Official County-Level Agricultural Estimates in the United States: Needs and Challenges Nathan B Cruze, Andreea L. Erciulescu, Balgobin Nandram, Wendy J. Barboza, and Linda J. Young
Response to discussions and a look ahead Vincent Dorie, Jennifer Hill, Uri Shalit, Marc Scott, and Dan Cervone
Comment on Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition Susan Gruber and Mark J. van der Laan
Strengthening Empirical Evaluation of Causal Inference Methods David Jensen
A Conversation With Robert E. Kass Sam Behseta
Two-sample instrumental variable analyses using heterogeneous samples Qingyuan Zhao, Jingshu Wang, Jack Bowden, and Dylan S Small
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
A Conversation with Noel Cressie Christopher K. Wikle and Jay M. Ver Hoef
Laplace’s theories of cognitive illusions, heuristics, and biases Andrew Gelman and Joshua Miller