The New England Journal of Statistics in Data Science (NEJSDS) invites submissions to a special issue of "Pushing the Boundary of Data Science through Statistical Modelling and Inference".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 particularly interested in novel methodologies and applications in data science that push the boundaries of statistical modelling and inference. We welcome submissions that explore innovative ways of applying statistical modelling and inference in data science, such as in the areas of biostatistics, environmental health, genetics, Bayesian statistics, spatial statistics, and others. We also encourage submissions that examine new data types, including functional data, big data, network data, and others, and that develop novel statistical methods for analyzing these data. The topics include (but not limited to)

• Bayesian methodology
• Biostatistics, Epidemiology, Genetics
• Machine Learning, Artificial Intelligence and Deep Learning
• Environmental Statistics
• Generalized linear models
• Survival analysis
• Longitudinal data analysis
• High-dimensional Inference, Regularization, and related areas
• Spatial/Spatio-temporal data
• Design of Experiments, Survey Sampling
• Multiple testing procedures
• Functional data analysis
• Network analysis
• Causal inference
• Robust statistics
• Nonparametric statistics
• Statistical computing

All submissions must be online through the website https://www.e-publications.org/ness/sbs/. Please state that your submissions are "Pushing the Boundary of Data Science through Statistical Modelling and Inference" in the Box of Comments to the editors.

Submission deadline: December 31, 2024

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 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 feel free to contact us.

We look forward to receiving your papers in due course.

• Feng Guo (Guest Editor), feng.guo@vt.edu
• Jyotishka Datta (Guest Editor), jyotishka@vt.edu