New Advances in Computational Statistics and Extreme Value Theory
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".
Deadline for manuscript submissions: 31 July 2026 | Viewed by 683
Special Issue Editor
Special Issue Information
Dear Colleagues,
Recent years have witnessed rapid advances in computational statistics and extreme value theory (EVT), driven by growing demands for robust statistical methods to address challenges in climate science, hydrology, finance, engineering, and other applied domains. Developments in high-performance computing, simulation-based methods, and machine learning techniques have opened new opportunities for modeling, estimation, and inference, particularly in analyzing rare events and extremes with significant real-world impacts.
This Special Issue aims to bring together original research articles, methodological developments, and comprehensive reviews that highlight the synergy between computational statistics and EVT. We are particularly interested in contributions that introduce innovative algorithms, computational frameworks, and hybrid approaches to tackle problems involving extremes, dependence structures, and uncertainty quantification. Applications that demonstrate the practical utility of these methods across disciplines—ranging from risk assessment, climate and hydrological extremes, environmental and financial modeling, to industrial process control—are strongly encouraged.
Potential topics include, but are not limited to, the following:
- Advances in computational methods for EVT and order statistics;
- Copula-based multivariate modeling of extremes;
- Simulation, resampling, and Bayesian methods for rare-event analysis;
- Ensemble machine learning and AI-driven approaches to extremes;
- Applications in hydrology, meteorology, climate risk, and finance;
- Uncertainty quantification and risk assessment for decision support.
We warmly invite researchers to contribute to this Special Issue and share their latest findings at the interface of computational statistics and extreme value theory.
Dr. Piyapatr Busababodhin
Guest Editor
Manuscript Submission Information
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Keywords
- computational statistics
- extreme value theory
- copula models
- order statistics
- bayesian methods
- machine learning for extremes
- risk assessment
- hydrology and climate applications
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