Statistical Methods for Forecasting and Risk Analysis
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".
Deadline for manuscript submissions: 31 December 2024 | Viewed by 1512
Special Issue Editors
Interests: discrete choice models; regression modeling; survival analysis; imbalanced datasets
Special Issues, Collections and Topics in MDPI journals
Interests: regression modeling; imbalanced datasets; variable selection; time series models
Interests: SAFE (sustainable, accurate, fair and explainable) artificial intelligence methods; machine learning model validation methods; assessment of operational and cyber risks; dependence analysis; sub-sampling methods; inequality measures for income distributions
Special Issue Information
Dear Colleagues,
In recent years, the fast accumulation and growing availability of data and computational power have led to the spread of sophisticated statistical and machine learning models, together with artificial intelligence-based systems.
While these methods have gained great popularity in data science applications, due to their capability of giving rise to powerful predictions, they have an intrinsic black box nature, where input data are elaborated through complex processes without effective control and monitoring of the operational, reputational, and strategic risks emerging from potential biases in forecasting.
Therefore, the use of statistical methods to predict financial, default, funding loss, and diagnostic-related risks (among others) requires the main criteria supporting the predictions to be known for assessing the related severity and fostering ad hoc measures to reduce them in the presence of shocks.
This Special Issue aims to share recent research, stimulate discussion, and disclose new research directions regarding the use of statistical techniques and AI systems to address the challenges which arise in forecasting and risk analysis. A particular focus will be devoted to papers (theoretical, empirical, and review) drawing attention to the application of models, computational techniques in estimation, simulation, and prediction in risk analysis contributing to decision making in various contexts (banks, firms, industries, transports, environmental and healthy institutions, higher education institutions, etc.).
Prof. Marialuisa Restaino
Prof. Marcella Niglio
Prof. Emanuela Raffinetti
Guest Editors
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- risk analysis
- forecasting and accuracy
- statistical modeling
- model selection
- applied statistics
- machine learning
- artificial intelligence
- massive dataset
- high (and ultra-high)-dimensional dataset
- variable selection
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