Statistics and Its Interface
ISSN Print 1938-7989 ISSN Online 1938-7997
4 issues per year
Ming-Hui Chen (University of Connecticut)
Yuedong Wang (University of California at Santa Barbara)
Aims and ScopeExploring the interface between the field of statistics and other disciplines, including but not limited to: biomedical sciences, geosciences, computer sciences, engineering, and social and behavioral sciences. Publishes high-quality articles in broad areas of statistical science, emphasizing substantive problems, sound statistical models and methods, clear and efficient computational algorithms, and insightful discussions of the motivating problems.
Publishing since 2008.
4 issues per year.
Indexing / Reviewing
Citation Report Metrics
Journal Citation Reports: Clarivate Analytics
Coverage Year: 2020
Total Citations: 757
Journal Impact Factor: 0.582
5-Year Impact Factor: 0.827
Immediacy Index: 0.167
Statistics and Its Interface is partially sponsored by the Yau Mathematical Sciences Center (MSC) of Tsinghua University.
Call for Papers: Special Issue on Statistical Learning of Tensor Data
Statistics and Its Interface (SII) invites submissions for a special issue on statistical learning of tensor data. Tensor, or multidimensional array, is arising in a wide range of scientific and business applications. Research on learning of tensor data has been rapidly expanding during the last few decades, extending to modern datasets such as medical images, social network, and personalized recommendation systems, and widely used in many fields including medicine, biology, public health, engineering, finance, economics, sports analytics, and environmental sciences. The rapid developments also lead to many challenges in estimation, inference, prediction, and computation in learning of tensor data. SII promotes interface between statistical theory, methodology and applications. Thus, we strongly encourage innovative theory, methodology and novel applications in statistical learning of tensor data. The review papers related with statistical learning of tensor data are also welcomed. Your papers, once accepted, will be published together in a special issue of SII.
The submission deadline for the special issue is October 1, 2022. All submissions must be online through the website http://www.e-publications.org/ip/sbs/index.php/index/login. Please state that your submissions are “For the Special Issue on statistical learning of tensor data” in the Box of Comments to the editors. The submissions will go through regular review process. As the editors for this special issue, we will handle the peer review timely and carefully.
With your support and collaboration, we are confident that the special issue will be a success that will reflect the state-of-art of research at the frontier of this vital and rapidly developing area. We look forward to receiving your papers in due course.
- Guanyu Hu (Co-Guest Editor), University of Missouri
- HaiYing Wang (Co-Guest Editor), University of Connecticut
- Jing Wu (Co-Guest Editor), University of Rhode Island
- Anru Zhang (Co-Guest Editor), Duke University
- Ming-Hui Chen (Co-Editor-in-Chief), University of Connecticut
- Yuedong Wang (Co-Editor-in-Chief), University of California, Santa Barbara