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Papers to Appear in Subsequent Issues

A Paradox From Randomization-Based Causal Inference

Peng Ding

Understanding Ding’s Apparent Paradox

Peter M. Aronow and Molly R. Offer-Westort

Randomization-Based Tests for "No Treatment Effects"

EunYi Chung

Inference from randomized (factorial) experiments

Rosemary Anne Bailey

Logistic Regression: From Art to Science

Dimitris Bertsimas and Angela King

Principles of Experimental Design for Big Data Analysis

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

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

Comparison and Assessment of Epidemic Models

Gavin Jarvis Gibson, George Streftaris, and David Thong

Models for the assessment of treatment improvement: the ideal and the feasible

Pedro César Álvarez-Esteban, Eustasio del Barrio, Juan Antonio Cuesta-Albertos, and Carlos Matrán

Bayesian nonparametrics for stochastic epidemic models

Theodore Kypraios and Philip O'Neill

Rejoinder to the comments on "A paradox from randomization-based causal inference"

Peng Ding

Approximate Bayesian Computation and simulation based inference for complex stochastic epidemic models

Trevelyan J. McKinley, Ian Vernon, Ioannis Andrianakis, Nicky McCreesh, Jeremy E. Oakley, Rebecca N. Nsubuga, Michael Goldstein, and Richard G. White

Sufficientness postulates for Gibbs-type priors and hierarchical generalizations

Sergio Andrés Bacallado, Marco Battiston, Stefano Favaro, and Lorenzo Trippa
Covariance Models for Global Spatial Statistics Jaehong Jeong, Mikyoung Jun, and Marc Genton

The general structure of evidence factors in observational studies

Paul R Rosenbaum

Hierarchical Sparse Modeling: A Choice of Two Group Lasso Formulations

Xiaohan Yan and Jacob Bien

Instrumental Variable Estimation with a Stochastic Monotonicity Assumption

Dylan Small, Zhiqiang Tan, Roland Ramsahai, Scott Lorch, and Alan Brookhart

The coordinate-based meta-analysis of neuroimaging data

Pantelis Samartsidis, Silvia Montagna, Thomas E. Nichols, and Timothy D. Johnson

On a General Definition of Depth for Functional Data

Irène Gijbels and Stanislav Nagy

Correction to "A Topologically Valid Definition of Depth for Functional Data"

Alicia Nieto-Reyes and Heather Battey

Contemporary views on the 2x2 binomial trial

Enrico Ripamonti, Chris Lloyd, and Piero Quatto

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