Empirical modeling
- Submission status
- Open
- Submission deadline
Empirical modeling involves the development of models that explain, predict, or simulate a particular aspect of the world, rather than purely theoretical or abstract principles. Empirical modeling starts with real-world data and observations, and then builds frameworks that are calibrated and validated against datasets. Specifically, statistical analysis and simulation techniques are employed to extract patterns and inferences, and to test hypotheses about how different variables interact. Empirical models are applied in a wide range of fields, including economics, epidemiology, environmental science, social sciences, and engineering, providing valuable insights and support for decision-making in each. More importantly, empirical modeling is iterative and dynamic. As new data become available, models are refined and updated to improve their accuracy and relevance. This ongoing process of validation and recalibration is what makes empirical modeling particularly powerful in dealing with complex, evolving issues.
This Collection welcomes original research on developing more adaptable, interpretable, and predictive approaches via the integration of advanced statistical methods, machine learning algorithms, and data science principles.
Editors
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Lazaros Gallos, PhD
Rutgers University, USA
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Song-Ju Kim, PhD
Tokyo University of Science, Japan
