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Models for Longitudinal Data with Applications to Early Warning Systems

Workshops of the PRIN Project, Perugia, Italy, 19 September 2024, 11 September 2025 and 23 January 2026

Gebonden Engels 2026 9783032241405
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Samenvatting

This open access book addresses key methodological and applied issues in rare event forecasting, with a particular focus on early warning systems based on hidden Markov models. It brings together recent advances developed by a cohesive and multidisciplinary group of researchers. A distinctive feature of the volume is its strong emphasis on real-world problems in economics, finance and health, illustrated using empirical datasets.

Specificaties

ISBN13:9783032241405
Taal:Engels
Bindwijze:Gebonden
Aantal pagina's:197
Uitgever:Springer Nature Switzerland
Hoofdrubriek:, Diversen
Thema:
K Economie, Financiën, Bedrijf en Management

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Inhoudsopgave

Chapter 1 Sampling-based and cost-sensitive classification in early warning systems for financial crises.- Chapter 2 Auto Machine Learning for Early Warning Crisis Detection.- Chapter 3 Exploring Binary Regression and Hidden Markov Models for Early Warning Systems.- Chapter 4 A regularized EWS for banking crises: a grouped fixed effects approach.- Chapter 5 A Bayesian Student’s t-Hidden Markov Model Approach for Cryptocurrencies Time Series.- Chapter 6 Link prediction in temporal networks: A dynamic stochastic block model approach.- Chapter 7 The substitution between primary and emergency care in individuals with chronic conditions: evidence from a structural model.- Chapter 8 The demand of primary and secondary care: a Bayesian hierarchical approach.

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          Models for Longitudinal Data with Applications to Early Warning Systems