Methodology and Application in Computational Statistics and Data Science
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".
Deadline for manuscript submissions: 31 May 2025 | Viewed by 27
Special Issue Editors
Interests: high-dimensional data modelling and inference; dimension reduction; variable selection; causal inference
Special Issue Information
Dear Colleagues,
Due to recent advancements in the fields of artificial intelligence and machine learning, massive amounts of data have been collected via various channels under different formats. While the growing availability of information would generally lead to significant scientific progression, it also presents great challenges in adequately analyzing such massive datasets. To this end, the scientific literature over various topics of computational statistics and data science have significantly grown in recent years. The following topics have gained increasing attentions: dimension reduction, feature screening, variable selection, optimal sampling, multitask learning, transfer learning, and distributed learning, among others. In this Special Issue, entitled “Methodology and Application in Computational Statistics and Data Science”, we invite papers to address both the methodological and computational aspects of dealing with challenges in the analysis of large datasets and new data types, including functional data, big data, network data, and others. We also welcome papers that specifically focus on applying the latest methods to the analysis of challenging datasets with complex structures, such as in the areas of biostatistics, genetics, spatial statistics, and others.
Dr. Wenbo Wu
Dr. Chenlu Ke
Guest Editors
Manuscript Submission Information
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Keywords
- statistical computing
- big data
- high-dimensional statistics
- dimension reduction
- feature screening
- variable selection
- sampling
