Papers to Appear in Subsequent Issues

When papers are accepted for publication, they will appear below. Any changes that are made during the production process will only appear in the final version. Papers listed here are not updated during the production process and are removed once an issue is published.

Modern Statistical Models and Methods for Estimating Fatigue-Life and Fatigue-Strength Distributions from Experimental Data William Q. Meeker, Luis A. Escobar, Francis G. Pascual, Yili Hong, Peng Liu, Wayne M. Falk, and Balajee Ananthasayanam
Analysis of Linked Files: A Missing Data Perspective Gauri Kamat and Roee Gutman
No need for an oracle:  the nonparametric maximum likelihood decision in the compound decision problem is minimax Ya’acov Ritov
On the Mixed-Model Analysis of Covariance in Cluster-Randomized Trials Bingkai Wang, Michael O. Harhay, Dylan S. Small, Tim P. Morris, and Fan Li
Multivariate Matérn Models — A Spectral Approach Drew Yarger, Stilian Stoev, and Tailen Hsing
Review of Quasi-Randomization Approaches for Estimation from Non-probability Samples Vladislav Beresovsky, Julie Gershunskaya, and Terrance D Savitsky
Randomized and Exchangeable Improvements of Markov’s, Chebyshev’s and Chernoff’s Inequalities Aaditya Ramdas and Tudor Manole
What You See is Not What is There: Mechanisms, Models and Methods for Point Pattern Deviations Peter Guttorp, Janine Illian, Joel Kostensalo, Mikko Kuronen, Mari Myllymäki, Aila Särkkä, and Thordis Thorarinsdottir
A Bayesian Practitioner’s Guide to Expectation Propagation Jackson Zhou, Clara Grazian, and John Thomas Ormerod
Markov Chain Monte Carlo Significance Tests Michael Howes
The Infinitesimal Jackknife and Combinations of Models Indrayudh Ghosal, Yunzhe Zhou, and Giles Hooker
Overdispersed Multinomial Models Antonio Forcina and Jose Manuel Pavia
Bayesian Nonparametric Inference for “Species-Sampling” Problems Cecilia Balocchi, Stefano Favaro, and Zacharie Naulet
On Matérn Covariance and Gaussian Markov Random Fields: A Spectral Analysis Patrick Edward Brown and Jamie Stafford
Overlap Measures Against Roc Summary Indices M.Carmen Pardo and Alba M. Franco Pereira
Evidence Bounds in Singular Models: Probabilistic and Variational Perspectives Anirban Bhattacharya, Debdeep Pati, Sean Plummer, and Yun Yang
Doubly Ranked Tests of Location for Grouped Functional Data Mark J Meyer
A Primer on Bayesian Neural Networks: Review and Debates Julyan Arbel, Konstantinos Pitas, Mariia Vladimirova, and Vincent Fortuin
An Overview of Asymptotic Normality in Stochastic Blockmodels:~Cluster Analysis and Inference Joshua Agterberg and Joshua Cape
Approximate Inference With Exponential Tilting Densities: Theory and Applications Xiaoping Shi, Xiang-Sheng Wang, Augustine Wong, and Wei Lin
A Conversation with Byron J.T. Morgan Rachel S. McCrea and Byron J. T. Morgan
Re-thinking Spatial Confounding in Spatial Linear Mixed Models Kori Khan and Candace Berrett
The Intersection of Location-Allocation, Partitional Clustering and Model-Based Clustering Techniques Tero Lähderanta, Lauri Lovén, Leena Ruha, Teemu Leppänen, Markku Kuismin, Ilkka Launonen, Susanna Pirttikangas, Jukka Riekki, and Mikko Sillanpää
A Note on Distance Variance for Categorical Variables Qingyang Zhang
A Unified Theory of Exact Inference and Learning in Exponential Family Latent Variable Models Sacha Sokoloski
Jeffreys-Prior Penalty for High-Dimensional Logistic Regression: A Conjecture About Aggregate Bias Ioannis Kosmidis and Patrick Zietkiewicz
Posterior Ramifications of Prior Dependence Structures Luke Hagar and Nathaniel Tyler Stevens
Conditionality Principle Under Unconstrained Randomness Vladimir Vovk
Choosing Alpha Post Hoc: The Danger of Multiple Standard Significance Thresholds Jesse Hemerik and Nick W Koning
Evaluating Machine Learning Models in Non-Standard Settings: An Overview and New Findings Roman Hornung, Malte Nalenz, Lennart Schneider, Andreas Bender, Ludwig Bothmann, Bernd Bischl, Thomas Augustin, and Anne-Laure Boulesteix
A Review of Off-Policy Evaluation in Reinforcement Learning Masatoshi Uehara, Chengchun Shi, and Nathan Kallus
Gibbs Optimal Design of Experiments Antony Overstall, Jacinta Holloway-Brown, and James McGree
The Central Role of the Loss Function in Reinforcement Learning Kaiwen Wang, Nathan Kallus, and Wen Sun
The Two Cultures of Prevalence Mapping: Small Area Estimation and Model-Based Geostatistics Jon Wakefield, Peter Gao, Geir-Arne Fuglstad, and Zehang Li
On Weighted Orthogonal Learners for Heterogeneous Treatment Effects Paweł Morzywołek, Johan Decruyenaere, and Stijn Vansteelandt
Restricted Maximum Likelihood Estimation in Generalized Linear Mixed Models Luca Maestrini, Francis K.C. Hui, and Alan H. Welsh
High-Probability Minimax Lower Bounds Tianyi Ma, Kabir A. Verchand, and Richard J. Samworth
The Fundamental Limits of Structure-Agnostic Functional Estimation Sivaraman Balakrishnan, Edward Kennedy, and Larry Wasserman
The Theory of Online Control Elad Hazan and Karan Singh
Stochastic Approximation and Reinforcement Learning: The Interface and a Little Beyond Vivek Shripad Borkar
Offline Reinforcement Learning in Large State Spaces: Algorithms and Guarantees Nan Jiang and Tengyang Xie
Sample-based Planning and Learning with Function Approximation Tor Lattimore and Csaba Szepesvari
Sources of Uncertainty in Supervised Machine Learning – A Statisticians’ View Cornelia Gruber, Patrick Oliver Schenk, Malte Schierholz, Frauke Kreuter, and Göran Kauermann
A Conversation with Murad S. Taqqu Shuyang Bai, Herold Dehling, Piotr Kokoszka, Vladas Pipiras, Stilian Stoev, and Walter Willinger
Which Depth to Use to Construct Functional Boxplots? Stanislav Nagy, Tomáš Mrkvička, and Antonio Elias
Conversation with Stephen Buckland David L Borchers and Eric Rexstad
The Many Routes to the Ubiquitous Bradley-Terry Model Ian Hamilton, Nicholas Tawn, and David Firth
On the Statistical Complexity for Offline and Low-Adaptive Reinforcement Learning with Structures Ming Yin, Mengdi Wang, and Yu-Xiang Wang
A Diagnostic Function for the Accuracy of Bootstrap Confidence Intervals Bradley Efron
Interpretation of Local False Discovery Rates Under the Zero Assumption Daniel Xiang, Nikolaos Ignatiadis and Peter McCullagh
Partially Observable RL: Benign Structures and Simple Generic Algorithms Qinghua Liu and Chi Jin
On the Minimum Strength of (Unobserved) Covariates to Overturn an Insignificant Result Danielle Tsao, Ronan Perry and Carlos Cinelli
On the Lack of Weak Continuity of Chatterjee’s Correlation Coefficient Axel Bücher and Holger Dette
On Neighbourhood Cross Validation Simon Wood
Impact of Kolmogorov-Nagumo’s Average in Statistics and Related Fields Nidhin K
Replicable Bandits for Digital Health Interventions Kelly Wang Zhang, Nowell Closser, Anna L Trella and Susan A Murphy
Leveraging external data in the analysis of randomized controlled trials: a comparative analysis Gopal Kotecha, Daniel E. Schwartz, Steffen Ventz and Lorenzo Trippa
Unifying Boxplots: A Multiple Testing Perspective Bowen Gang, Homgmei Lin and Tiejun Tong
Introduction to the Special Issue on Reinforcement Learning Nan Jiang and Ambuj Tewari
Sampling, Diffusions, and Stochastic Localization Andrea Montanari
Nondimensionalizing physical and statistical models: a unified approach Tae Yoon Lee, James V Zidek, and Nancy Heckman
The diachronic Bayesian Vladimir Vovk
Identification and Classification of Highly-Cited Statistical Papers Michael J Schell and Ji-Hyun Lee
Inference with Sequential Monte-Carlo Computation of p-values: Fast and Valid Approaches Ivo V. Stoepker and Rui M. Castro
Evidential model averaging: Combining p-values and Bayes factors to propagate uncertainty about statistical methods and their assumptions David R. Bickel
Mixture priors for replication studies Roberto Macrì Demartino, Leonardo Egidi, Leonhard Held and Samuel Pawel
A New Objection to the Jeffreys Prior for the (Negative) Binomial Frank Tuyl, Richard Gerlach and Kerrie Mengersen
Calling Things by Their Proper Names in Statistics and Probability: Subjectivity and Objectivity Joseph B. Kadane