Call for a Special Issue on “Modern Bayesian Methods with Applications in Data Science”

This call for papers invites participants of the EAC-ISBA 2021 to submit their work to a special issue of The New England Journal of Statistics in Data Science (NEJSDS).

The NEJSDS is the official journal of the New England Statistical Society (NESS) that aims to serve as an interface between statistics and other disciplines in data science. The journal publishes high quality original research, novel applications, and timely review articles in all aspects of data science, including (but not limited to) all areas of statistical methodology, methods of machine learning, and artificial intelligence, novel algorithms, computational methods, data management and manipulation, applications of data science methods, among others. This special issue is on “Modern Bayesian Methods with Applications in Data Science”, in connection with the theme of the conference to celebrate Dr. James O. Berger’s 70th birthday.

The submission deadline for the special issue is February 28, 2022. All submissions must be online through the website https://www.e-publications.org/ness/sbs/. Please state that your submissions are “For the Special Issue on Modern Bayesian Methods with Applications in Data Science” in the Box of Comments to the editors. The submissions will go through a regular review process. As the editors for this special issue, we will handle the peer review timely and carefully.

NEJSDS is proud to be a pioneer in the reform of the traditional peer review process by implementing a new hybrid journal review process. In this new review process, authors have the option to supply referee reports invited by the authors, as a supplement to the traditional review reports led by the editorial board. The decisions will take into consideration of both review reports initiated by the authors and the reports initiated by the editorial board. If any authors choose to provide open reviews for their articles, please mention it in the cover letter and upload the review reports (including the information of reviewers’ affiliations and email addresses) as supplementary materials when submitting the article.

If you have any questions or have a pre-submission inquiry, please contact us at editors@nestat.org or e-meet us by booking a virtual meeting.

We look forward to receiving your papers in due course.

Dipak K. Dey (Guest Editor), University of Connecticut
Ming-Hui Chen (Co-Editor-in-Chief), University of Connecticut
Minge Xie (Co-Editor-in-Chief), Rutgers University