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Pennoni, F. (2026). Presentation of the Proceedings Book of the Project PRIN edited with Springer [Altro].

Presentation of the Proceedings Book of the Project PRIN edited with Springer

Pennoni, F
2026
Altro
Final Workshop PRIN 2022 Hidden Markov Models for Early Warning Systems Perugia 2026
This contribution presents the contents of the volume “Models for Longitudinal Data with Applications to Early Warning Systems”, developed through the collaboration of the research units involved in the PRIN 2022 project “Hidden Markov Models for Early Warning Systems.” The work focuses on the development and application of an advanced class of models for longitudinal data, with particular emphasis on Hidden Markov Models (HMMs), which are regarded as robust tools for forecasting rare events. Across eight chapters, the volume integrates traditional statistical inference approaches, Bayesian methods, and machine learning techniques to address key methodological challenges, including data imbalance, uncertainty, and cost-sensitive classification. The applications span multiple scientific domains, ranging from financial econometrics (banking crises and cryptocurrency volatility) to network science (link prediction in temporal networks), and health economics (analysis of demand for primary and secondary care). The results presented not only provide methodological innovations for Early Warning Systems (EWS), but also offer empirical evidence of strong policy relevance. In line with open science principles, the research is supported by reproducible code written in Python, R, and STATA.
English
2026
1
14
https://sites.google.com/view/hmm-ews/home?authuser=0
Pennoni, F. (2026). Presentation of the Proceedings Book of the Project PRIN edited with Springer [Altro].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/598702
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