Submission Deadline: 31 October 2026 View: 11 Submit to Special Issue
Prof. Dr. Christophe Chesneau
Email: christophe.chesneau@gmail.com
Affiliation: Department of Mathematics, Université de Caen Normandie, Caen, France
Research Interests: survival analysis, statistical modelling, computational statistics
Prof. Dr. Emrah Altun
Email: emrahaltun@gazi.edu.tr
Affiliation: Department of Statistics, Gazi University, Ankara, Turkey
Research Interests: statistical modeling, linear regression, distribution theory
The rapid advancement of computational power and algorithmic techniques has fundamentally transformed statistical research and practice. Computer modeling has become a central pillar of modern statistics, enabling complex data analysis, simulation-based inference, Bayesian computation, predictive modeling, and data-driven decision-making across a wide range of disciplines.
This special issue, "Computer Modeling in Statistics," highlights the latest research on methodological innovations, computational strategies, and real-world applications of computer-based statistical modeling. The issue aims to foster cross-disciplinary dialogue and demonstrate how computation continues to expand the boundaries of statistical thinking.
This special issue will feature theoretical, methodological, and applied papers that explore how computational modeling enhances statistical inference, model building, and data analysis. The objectives are to:
· Showcase novel computational algorithms for statistical modeling
· Explore the interplay between simulation, computation, and theory
· Present case studies where computer modeling resolves real-world statistical challenges
· Highlight the role of modern computing in high-dimensional, big data, and machine learning contexts
Contributions may include (but are not limited to):
· Computational Methods and Algorithms
· Monte Carlo methods, Markov Chain Monte Carlo (MCMC)
· Variational inference and stochastic optimization
· High-performance computing in statistics
· Parallel and distributed statistical computation
· Statistical Modeling Frameworks
· Bayesian hierarchical models
· Nonparametric and semiparametric modeling
· Agent-based and simulation-based modeling
· Dynamic and spatiotemporal models
· Applications and Case Studies
· Computer modeling in econometrics, epidemiology, and environmental statistics
· Computational approaches in bioinformatics and genomics
· Data-driven modeling in social and behavioral sciences
· Predictive modeling and decision-making systems
· Integration of machine learning and statistical computing
· Probabilistic programming and automated inference
· Statistical computing in artificial intelligence
· Reproducibility, open-source software, and computational ethics in statistics
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