Computational Statistics and Applications for High-Dimensional 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 December 2026 | Viewed by 93
Special Issue Editors
Interests: model selection in mixed models; generalized linear models; bootstrap methods; high-dimensional data; modeling diagnostics; multiple comparison procedures; bayesian inference
Special Issues, Collections and Topics in MDPI journals
Interests: spatial–temporal data analysis; machine learning; high-dimensional data analysis; Bayesian inference; explainable AI; nonparametric statistics; statistical process control
Special Issue Information
Dear Colleagues,
The recent development of computational capacity has reinforced high-dimensional data analysis, and many computational challenges in modeling, inference, and scalability have emerged simultaneously. To facilitate and overcome such challenges, more computational statistical methodologies and techniques for high-dimensional data have been developed and extensively applied in various fields, including bioinformatics, software engineering, temporal–spatial analytics, and environmental analytics. In this Special Issue, we welcome research work on computational statistical methods and their applications for high-dimensional data. We strongly encourage interdisciplinary work with real data analysis.
This Special Issue calls for papers in, but not limited to, the following areas:
- Computational techniques and algorithms (e.g., Monte Carlo methods, variational inference, bootstrapping, scalable optimization, and machine learning algorithms);
- Computational methodology developments that bridge statistical theory and practical applications;
- Computational techniques in statistical modeling for high-dimensional data and applications (e.g., regression, mixed models, mixture models, and generalized linear models);
- Statistical learning methods for high-dimensional data and applications (e.g., Lasso and related regularization methods, splines, trees, random forests, neural networks, clustering, and classification);
- Statistical computing and software for high-dimensional data;
- Dimension reduction and variable selection based on feature screening for high-dimensional data;
- Applications based on Bayesian inference and computational methods for high-dimensional data.
Prof. Dr. Junfeng Shang
Dr. Shenghao Xia
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
- statistical computing
- statistical modeling
- computational algorithms
- data visualization and modeling
- interdisciplinary applications
- variable selection
- feature screening
- Bayesian applications
- spatial–temporal analysis
- machine learning
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