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Papers to Appear in Subsequent Issues

A Paradox From Randomization-Based Causal Inference

Peng Ding

Penalising model component complexity: A principled, practical approach to constructing priors

Daniel Peter Simpson, Håvard Rue, Thiago Martins, Andrea Riebler, and Sigrunn H Sørbye

You just keep on pushing my ove over the borderline: a rejoinder

Daniel Peter Simpson, Håvard Rue, Thiago Martins, Andrea Riebler, and Sigrunn H Sørbye

Consistency of the MLE under mixture models

Jiahua Chen

Leave Pima Indians alone: binary regression as a benchmark for Bayesian computation

Nicolas Chopin and James Ridgway

Understanding Ding’s Apparent Paradox

Peter M. Aronow and Molly R. Offer-Westort

Combining Survey Data with Other Data Sources

Sharon L Lohr and Trivellore E Raghunathan

On the Sensitivity of the Lasso to the Number of Predictor Variables

Cheryl J. Flynn, Clifford M. Hurvich, and Jeffrey S. Simonoff

Forecaster's Dilemma: Extreme Events and Forecast Evaluation

Sebastian Lerch, Thordis L. Thorarinsdottir, Francesco Ravazzolo, and Tilmann Gneiting

Model-assisted survey estimation with modern prediction techniques

F. Jay Breidt and Jean D. Opsomer

Randomization-Based Tests for "No Treatment Effects"

EunYi Chung

Prior specification is engineering, not mathematics

James G Scott

A Conversation with Robert Groves

Hermann Habermann, Courtney Kennedy, and Partha Lahiri

A Conversation with Lynne Billard

Nitis Mukhopadhyay

Options for Conducting Web Surveys

Matthias Schonlau and Mick P. Couper

Inference for Non-probability Samples

Michael R Elliott and Richard Valliant

J. B. S. Haldane's Contribution to the Bayes Factor Hypothesis Test

Alexander Etz and Eric-Jan Wagenmakers

Inference from randomized (factorial) experiments

Rosemary Anne Bailey

Swinging for the fence in a league where everyone bunts

James Steven Hodges

Logistic Regression: From Art to Science

Dimitris Bertsimas and Angela King

Some comments about "Penalising model component complexity: A principled, practical approach to constructing priors" by Simpson, Rue, Martins, Riebler, and Sørbye

Christian P Robert and Judith Rousseau

Principles of Experimental Design for Big Data Analysis

Christopher C Drovandi, Christopher Holmes, James McGree, Kerrie Mengersen, Sylvia Richardson, and Elizabeth Ryan

Fitting Regression Models to Survey Data

Thomas Lumley and Alastair Scott

Probability Sampling Designs: Balancing and Principles for Choice of Design

Yves Tille and Matthieu Wilhelm

Towards automated prior choice

David Dunson

Construction of weights in surveys: a review

David Haziza and Jean-François Beaumont

Approaches to Improving Survey-Weighted Estimates

Qixuan Chen, Michael R. Elliott, David Haziza, Ye Yang, Malay Ghosh, Roderick J.A. Little, Joseph Sedransk, and Mary Thompson

An Apparent Paradox Explained

Wen Wei Loh, Thomas Stuart Richardson, and James M. Robins

Importance Sampling: Computational Complexity and Intrinsic Dimension

Omiros Papaspiliopoulos, Sergios Agapiou, Daniel Sanz-Alonso, and Andrew M Stuart

Estimation of causal effects with multiple treatments: a review and new ideas

Michael J. Lopez and Roee Gutman

On the choice of difference sequence in a unified framework for variance estimation in nonparametric regression

Wenlin Dai, Tiejun Tong, and Lixing Zhu

Introduction to the Design and Analysis of Complex Survey Data

Chris Skinner and Jon Wakefield

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