Statistics and Its Interface

Special Notices

Invitation to Submit: Special Issue on Statistical Learning of Multivariate Data Analysis

We are pleased to invite submissions for a special issue Statistics and Its Interface focusing on recent advances in statistical learning of multivariate data analysis.

Statistics and Its Interface explores the intersection between the field of statistics and other disciplines, publishing high-quality articles across broad areas of statistical science. The journal emphasizes substantive problems, sound statistical models and methods, clear and efficient computational algorithms, and insightful discussions of motivating problems. This special issue aims to review and compile recent advances in statistical learning of multivariate data.

The main topics covered in this special issue include both traditional multivariate regression analysis such as multivariate distribution theory, multivariate regression models, discrimination and classification analysis, factor analysis, clustering, and covariance models, as well as contemporary multivariate analysis. Topics in the latter category include high-dimensional data analysis, dimension reduction techniques, statistical inference, machine learning and statistical learning for multivariate data, network data analysis, image data analysis, and more.

As is customary withStatistics and Its Interface, the focus is on the development and evaluation of novel statistical methodologies for multivariate data. These methodologies may be motivated by practical or theoretical problems and driven by principles of statistical learning and machine learning. All methods should be validated through standard mathematical arguments, which may be complemented by asymptotic arguments or computer-based experiments. Illustrations with relevant and original data analysis are highly encouraged.

The deadline for manuscript submission April 30, 2025. Submissions should clearly indicate that they are intended for the special issue on statistical learning of multivariate data analysis and must be submitted through SII’s electronic journal management system. Further guidance about the structure, length, and format of manuscripts can be found on the journal webpage: See Statistics and Its Interface Submission Guidelines. All submissions will go through the regular review process. As the guest editors for this special issue, we will handle the peer review process carefully and in a timely manner.

We are confident that with your support and collaboration this special issue will be a success, and will reflect state-of-the-art research at the frontier of this vital and rapidly developing area. We look forward to receiving your papers.

Guest Editors:

• Prof. Bingyi Jing (), Southern University of Science and Technology of China;
• Prof. Jinyuan Chang (), Southwestern University of Finance and Economics
• Prof. Xingdong Feng (), Shanghai University of Finance and Economics;
• Prof. Tian Bobing (), Harbin Institute of Technology, China;
• Prof. Jiang Xuejun (), Southern University of Science and Technology, China.