Application of Multivariate Modeling in the Social and Health Sciences
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematical Biology".
Deadline for manuscript submissions: 31 December 2024 | Viewed by 1679
Special Issue Editor
Special Issue Information
Dear Colleagues,
The high-dimensional nature of social and health science data has received increasing attention from both academia and industry since the development of supportive hardware and algorithms. The multivariate analysis methodology aims to properly understand the high-dimensional data in a statistical, mathematical, and numerical format. Data could be collected through observational studies, well-designed experiments, or pre-existing databases. The multivariate analysis enables practitioners to grasp complicated concepts retrieved from decoding intertwined relationships among variables. Integration of statistical modeling and machine learning can help decision-makers to gain a significant advantage in core competencies. As a result, today’s practitioners can adopt a holistic approach to adopting either machine learning or statistical methodology as data science to optimize ways of translating data into knowledge.
This Special Issue of Multivariate Modeling in the Social and Health Sciences is intended to present the recent advances in high-dimensional data management in the social and health fields. Authors are encouraged to submit applied articles addressing this theme in this Special Issue. Research articles, review articles, as well as short communications, are all welcomed. Topics include, but are not limited to, the following research topics:
- Impact of multivariate data analysis on decision-making process regarding social and health science;
- Multivariate methodology development in social and health science;
- Feature extraction and selection for high-dimensional social and health data;
- Data visualization for multivariate data analysis and data quality;
- Multivariate analysis for the impact of COVID-19 on social and health field.
Dr. Yen-Kuang Lin
Guest Editor
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Keywords
- longitudinal data
- interpretability
- sample size issue
- practical significance
- data reduction
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