Advances in Multivariate Statistical 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 March 2027 | Viewed by 125
Editor
Interests: mathematical statistics; data analysis (environmental, agricultural, medical); mathematical modelling and simulation; new computer technology development; system analysis (environmental water pollution); computing mathematics
Special Issue Information
Dear Colleagues,
Multivariate statistics and data analysis have become essential tools for extracting meaningful information from the increasingly complex and high-dimensional datasets encountered in science, engineering, economics, medicine, and environmental studies. Recent advances in this field have focused on developing robust statistical methodologies, efficient computational algorithms, and data-driven approaches capable of handling large-scale, heterogeneous, and dependent data structures.
Contemporary research encompasses multivariate hypothesis testing, dimension reduction techniques, clustering and classification methods, Bayesian and sequential inference, machine learning integration, change-point detection, and analysis of correlated and spatio-temporal data. Particular attention is devoted to controlling error rates in multiple testing problems, developing adaptive and sequential decision-making procedures, and constructing models that effectively capture complex dependence structures among variables.
The growing availability of big data has stimulated the creation of new statistical frameworks combining classical multivariate analysis with modern computational methods, including artificial intelligence and data mining techniques, developments which provide researchers and practitioners with powerful tools for pattern recognition, uncertainty quantification, prediction, and decision support across a wide range of applications.
Advances in multivariate statistics continue to enhance our ability to analyze complex systems, improve inferential accuracy, and address emerging challenges in data-intensive scientific research.
Prof. Dr. Kartlos Kachiashvili
Guest Editor
Manuscript Submission Information
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Keywords
- multivariate statistics
- data analysis
- statistical inferences
- dimension reduction
- clustering and classification methods
- machine learning integration
- change-point detection
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