Special Issue "Statistical learning and Its Applications"
Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 1230
Interests: mixture models; distribution theory; EM algorithm; univariate; multivariate and copula-based claim frequency
The past decade has seen the emergence of a range of new technologies and big data analytics which have begun to reshape the landscape of predictive modeling in practice and in research in many areas.
Statistical learning combines computational statistics with machine learning techniques and can provide excellent predictive performances without the tedious procedure of feature engineering and learn non-linearities in the input data and interactions between these. Thus, it can enable researchers to develop a framework for analyzing the stylized characteristics of data sets across an abundance of different areas, such as biology, epidemiology, criminology, meteorology, seismology, sports science, decarbonization, forecasting of weather-related hazards due to climate change, finance, and insurance.
This Special Issue aims to showcase novel applications of the most recent statistical learning techniques for developing better data-driven methods on studying practical problems in the aforementioned areas. Special emphasis should be given to hybrid models, which are based on combinations of neural networks (NNs) with statistical models. Hybrid models can have spectacular applications due to their ability to model large data sets with a large number of input features and because they can directly deal with unstructured data instead of structuring, or aggregating, which results in losing individual information. An outstanding issue is understanding how they overcome the caveat of dimensionality to generate or classify data.
Dr. Dimitrios Christopoulos
Dr. George Tzougas
Manuscript Submission Information
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- statistical learning
- computational statistics
- machine learning
- data-driven methods