Advances in High-Dimensional Statistics
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".
Deadline for manuscript submissions: closed (30 November 2024) | Viewed by 1117
Special Issue Editor
Special Issue Information
Dear Colleagues,
High-dimensional data analysis has emerged as a critical area of research with applications in fields such as machine learning, bioinformatics, and finance. The complexity and scale of high-dimensional datasets pose unique challenges that demand innovative statistical methodologies and computational approaches. This call for paper contributions invites researchers, academics, and practitioners to share their latest findings, methodologies, and applications in the realm of high-dimensional statistics.
We welcome submissions that address a broad range of topics related to advances in high-dimensional statistics, including but not limited to the following:
- Dimensionality reduction techniques;
- Sparse and low-rank modeling;
- High-dimensional inference;
- High-dimensional variable selection;
- Graphical models for high-dimensional data;
- Bayesian methods for high-dimensional problems;
- High-dimensional clustering and classification;
- Nonparametric approaches for high-dimensional data;
- Computational challenges and scalable algorithms;
- Applications of high-dimensional statistics in various domains (e.g., biology, economics, image analysis, etc.).
Dr. Yuwen Gu
Guest Editor
Manuscript Submission Information
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Keywords
- dimensionality reduction techniques
- sparse and low-rank modeling
- high-dimensional inference
- high-dimensional variable selection
- graphical models for high-dimensional data
- Bayesian methods for high-dimensional problems
- high-dimensional clustering and classification
- nonparametric approaches for high-dimensional data
- computational challenges and scalable algorithms
- applications of high-dimensional statistics in various domains (e.g., biology, economics, image analysis, etc.)
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