Book

One of the main research outputs of the project is a book published with Springer. The provisional list of chapters follows.

Book chapters

1. Mircoli, A., Pigini, C., and Potena, D. (2026). Sampling-based and cost-sensitive classification in early warning systems for financial crises. In F. Bartolucci, P. Li Donni, F. Pennoni, and C. Pigini (Eds.), Models for Longitudinal Data with Applications to Early Warning Systems, pp. 1–18, Springer-Verlag.

2. Cesarini, M., Brusa, L., Pennoni, F., and Vittadini, G. (2026). Auto machine learning for early warning crisis detection. In F. Bartolucci, P. Li Donni, F. Pennoni, and C. Pigini (Eds.), Models for Longitudinal Data with Applications to Early Warning Systems, pp. 19-51, Springer-Verlag.

3. Brusa, L., Pennoni, F., Peruilh Bagolini, R., and Bartolucci, F. (2026). Exploring binary regression and hidden Markov models for early warning systems. In F. Bartolucci, P. Li Donni, F. Pennoni, and C. Pigini (Eds.), Models for Longitudinal Data with Applications to Early Warning Systems, pp. 52-81, Springer-Verlag.

4. Pigini, C., and Pionati, A. (2026). A regularized EWS for banking crises: a grouped fixed effects approach. In F. Bartolucci, P. Li Donni, F. Pennoni, and C. Pigini (Eds.), Models for Longitudinal Data with Applications to Early Warning Systems, pp. 82-109, Springer-Verlag.

5. Bagolini, R. P., Tancini, D., and Pandolfi, S. (2026). A Bayesian Student’s t-Hidden Markov Model Approach for Cryptocurrencies Time Series. In F. Bartolucci, P. Li Donni, F. Pennoni, and C. Pigini (Eds.), Models for Longitudinal Data with Applications to Early Warning Systems, pp. 110-130, Springer-Verlag.

6. Pandolfi, S., Brusa, L., Pennoni, F., and Bartolucci, F. (2026). Link prediction in temporal networks: A dynamic stochastic block model approach. In F. Bartolucci, P. Li Donni, F. Pennoni, and C. Pigini (Eds.), Models for Longitudinal Data with Applications to Early Warning Systems, pp. 131-150, Springer-Verlag.

7. Laudicella, M., and Li Donni, P. (2026). The substitution between primary and emergency care in individuals with chronic conditions: evidence from a structural model. In F. Bartolucci, P. Li Donni, F. Pennoni, and C. Pigini (Eds.), Models for Longitudinal Data with Applications to Early Warning Systems, pp. 152-181, Springer-Verlag.

8. Li Donni, P., and Nicodemo, C. (2026). The demand of primary and secondary care: a Bayesian hierarchical approach. In F. Bartolucci, P. Li Donni, F. Pennoni, and C. Pigini (Eds.), Models for Longitudinal Data with Applications to Early Warning Systems, pp. 182-204, Springer-Verlag.