Book

One of the main research outputs of the project is a book published with Springer:

Bartolucci, F., Li Donni, P., Pennoni, F., and Pigini, C. (2026), Models for longitudinal data with applications to Early Warning Systems, Springer, ISBN 978-3-032-24140-5. 

Provisional list of 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. Peruilh Bagolini, R., 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.