New Challenges in Statistical Analysis and Multivariate Data Analysis
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".
Deadline for manuscript submissions: 31 May 2026 | Viewed by 32
Special Issue Editors
Interests: circular data; multivariate analysis; bioinformatics; stochastic process
Special Issue Information
Dear Colleagues,
This Special Issue welcomes high-quality original research and review articles focused on recent developments and ongoing challenges in statistical and multivariate data analysis. We encourage submissions that present novel methodologies, cutting-edge computational techniques, and interdisciplinary applications across fields such as environmental science, public health, economics, and the social sciences.
Topics of interest include high-dimensional data analysis, robust and nonparametric methods, machine learning integration, and advanced multivariate modeling techniques.
The goal of this Issue is to provide a platform for researchers and practitioners to share innovative ideas, promote cross-disciplinary collaboration, and contribute to the advancement of modern data analysis.
Dr. Sungsu Kim
Dr. Chi Seng Pun
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- multivariate data
- machine learning
- high-dimensional data
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