Advances in High-Dimensional Data Analysis
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".
Deadline for manuscript submissions: 31 December 2024 | Viewed by 1624
Special Issue Editors
Interests: statistical learning; survival analysis and data mining
Interests: machine learning; spatial statistics; high-dimensional and complex data analysis; deep learning; kriging methods; manifold learning
Special Issue Information
Dear Colleagues,
High-dimensional data analysis has been an important focus within theoretical and applied statistics research for more than three decades, with applications areas including biostatistics, bioinformatics, chemistry, ecology, economy, and social sciences. The aim of this Special Issue is to collect research papers that use statistical (methodological, theoretical, or computational) principles for high-dimensional data analysis, as well as scalable optimization methods and applications in important real-world fields.
In this Special Issue, we encourage original research submissions that provide new results in the setting of high-dimensional statistical inference and their applications. Review papers within all aspects of high-dimensional data analysis are also welcome.
Dr. Hong Wang
Dr. Liang Shen
Dr. Xuewei Cheng
Guest Editors
Manuscript Submission Information
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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
- high-dimensional inference
- feature screening
- variable selection
- dimension reduction
- high-dimensional statistical learning
- machine learning for high-dimensional data
- various applications of high-dimensional analysis approaches
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