Robust Statistics in the Modern Era: From Classical Foundations to AI-Driven Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".
Deadline for manuscript submissions: 20 October 2026 | Viewed by 218
Special Issue Editor
Special Issue Information
Dear Colleagues,
Robust statistics has long played a central role in ensuring reliable inference in the presence of outliers, model misspecification, heavy-tailed distributions, and contaminated data. From its classical foundations in robust estimation and hypothesis testing, the field has continued to evolve in response to the increasing complexity, scale, and heterogeneity of modern data. Today, robust statistical methods are more relevant than ever, particularly in the context of high-dimensional data, large-scale computation, and artificial intelligence-driven applications.
This Special Issue will bring together researchers working on theoretical developments, methodological innovations, and practical applications of robust statistical techniques. We seek contributions that advance the understanding of robustness in both classical and modern settings, as well as papers that demonstrate the impact of robust methods in real-world problems arising in data science, machine learning, and AI, including applications in environmental sciences where data are often noisy, incomplete, and subject to extreme events.
We invite original research articles, methodological papers, and applied studies on topics including, but not limited to, the following:
- Robust estimation, testing, and inference;
- Robust methods for high-dimensional, spatial, temporal, and functional data;
- Robust regression, classification, and clustering;
- Robust optimization and regularization techniques;
- Robustness in machine learning and deep learning models;
- Robust statistical learning under distributional shifts and adversarial settings;
- Computational aspects of robust methods;
- Applications of robust statistics in AI, environmental sciences, finance, biomedical sciences, engineering, and social sciences.
By highlighting both foundational theory and emerging AI-driven applications, this Special Issue will showcase the breadth, depth, and ongoing relevance of robust statistics in the modern era. We welcome contributions from statisticians, data scientists, and researchers from related disciplines who are developing or applying robust statistical methodologies to address contemporary data challenges.
Dr. Evrim Oral
Guest Editor
Manuscript Submission Information
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Keywords
- robust statistical methods
- statistical learning and AI
- outliers
- contaminated data
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