A Special Issue of New England Journal of Statistics in Data Science (NEJSDS)

The New England Journal of Statistics in Data Science (NEJSDS) invites original research articles for a Special Issue on Foundational Concepts in Bayesian, Fiducial, and Frequentist (BFF) Statistics.

The explosion of data-driven science and the rapid advance of machine learning and AI algorithms have brought in focus the importance of statistical principles and foundational methods.

Information about the Special Issue

Guest editors:

• Radu V Craiu, University of Toronto
• Jan Hannig, University of North Carolina, Chapel Hill
• Frank Konietschke, Charite Berlin

This special issue seeks to clarify, unify, and extend foundational ideas across the three BFF schools of thought in Statistics, with particular emphasis on:

• Foundational statistical principles applied to data science, machine learning, and artificial intelligence models
• Novel methodological developments grounded in foundational principles

This Special Issue is associated with the Bayesian, Fiducial and Frequentist Conference that took place July 10-11, 2026 at the University of Salzburg, Austria.

Important Dates

• Intent to Submit (optional): October 1, 2026
• Manuscript Submission Deadline: January 15, 2027
• First-Round Reviews Complete: April 15, 2027
• Revised Manuscripts Due: June 30, 2027
• Final Decisions: September 1, 2027
• Expected Publication: Late 2027 / Early 2028

Format

The articles submitted to the special issue must respect the formatting rules of the New England Journal of Statistics in Data Science.