Articles, book chapters and conference proceedings

Published articles

1. Brusa, L., Pennoni, F., and Bartolucci, F. (2024), Maximum likelihood estimation for discrete latent variable models via evolutionary algorithms, Statistics and Computing, 34, 1-15.

2. Pennoni, F., Bartolucci, F., and Pandolfi, S. (2024), Variable selection for hidden Markov models with continuous variables and missing data, Journal of Classification, 41, 568-589.

3. Bartolucci, P. Favaro, D. Pennoni, F., Sciulli, D. (2024), An analysis of the effect of streaming on civic participation through a causal hidden Markov model, Social Indicator Research, 172, 173-190.

4. Cortese, F., Pennoni, F., and Bartolucci, F. (2024), Maximum likelihood estimation of multivariate regime switching Student-t copula models, International Statistical Review, 92, 327-354.

5. Laudicella, M., Li Donni, P., and Prete, V. (2024), Healthcare utilisation by diabetic patients in Denmark: the role of primary care in reducing emergency visits, Health Policy, 145, 105079. 

6. Brusa, L. and Matias, C. (2024), Model-based clustering in simple hypergraphs through a stochastic blockmodel, Scandinavian Journal of Statistics, 51, 1661-1684.

7. Bartolucci, F., Pandolfi, S., and Pennoni, F. (2025), On a class of finite mixture models that includes hidden Markov models, Journal of Multivariate Analysis, 105423.

8. Brusa, L. and Pennoni, F. (2025), Variational inference for estimating dynamic stochastic block models through an evolutionary algorithm. Advances in Data Analysis and Classification, 19, 469–492.

9. Pennoni, F., Pandolfi, S., and Bartolucci, F. (2025), LMest: An R package for estimating generalized latent Markov models. The R Journal, 16, 71-101.

10. Gomez-Ruiz, M., Li Donni, P., and Marino, M. (2025), Dynamics of protest: Understanding violent and nonviolent protest in Africa, European Journal of Political Economy, 90, 102745. 

11. Bartolucci, F., Li Donni, P., and Pandolfi, S. (2026), Bivariate zero-inflated stochastic block models for the analysis of longitudinal hospital network data, Journal of the Royal Statistical Society, Series A, qnag026.

12. Bartolucci, F., Pandolfi, S., and Pennoni,F. (2026), Parsimonious parametrizations of transition matrices of Markov chain and hidden Markov models, Annals of Operations Research.

Book chapters and conference proceedings

1. Bagolini, R. P., Tancini, D., Bartolucci, F., and Pandolfi, S. (2025), A Data Augmented Bayesian Approach for Rectangular Hidden Markov Models,  In: di Bella, E., Gioia, V., Lagazio, C., Zaccarin, S. (eds.), Statistics for Innovation IV. SIS 2025. Italian Statistical Society Series on Advances in Statistics, Springer, pp. 189-194. ISBN: 978-3-031-96032-1. (preprint)

2. Bartolucci, F., Greenacre, M., Pandolfi, S., and Pennoni, F. (2025), Hidden Markov and related discrete latent variable models: An application to compositional data. In: Giordano, G., La Rocca, M., Niglio, M., Restaino, M., Vichi, M. (eds.), Studies in Classification, Data Analysis and Knowledge Organization. CLADAG 2023, Springer, pp. 1-8. ISBN: 978-3-031-84701-1. (preprint)

3. Brusa L., Pennoni F., Bartolucci F., and Peruilh Bagolini R. (2025), Prediction of early warning crises by a hidden Markov model with covariates. In: Pollice, A., Mariani, P. (eds.), Methodological and Applied Statistics and Demography II, SIS 2024, Short Papers, Solicited Sessions, Springer, pp. 146–152. ISBN: 978-3-031-64350-7. (preprint)

4. Brusa, L., Pennoni, F., Bartolucci, F., and Maggi, L. (2025), Dynamic Classification Through Three-Step Estimation: Evidence from a Multinational Longitudinal Study of Myasthenia Gravis Patients. In: di Bella, E., Gioia, V., Lagazio, C., Zaccarin, S. (eds.), Statistics for Innovation II, SIS 2025, Short Papers, Contributed Sessions 1, Sprigner, pp. 270–276. ISBN: 978-3-031-96303-2. (preprint)

5. Brusa, L., and Pennoni, F. (2025), Stochastic Block Model Based on Variational Inference and its Extensions: An Application to Examine Global Migration Dynamics. In: Nakai, M. (ed.), Advances in Quantitative Approaches to Sociological Issues. Behaviormetrics: Quantitative Approaches to Human Behavior, Springer, 1–27. ISBN: 978-981-96-7109-0. (preprint)

6. Nakai, M. and Pennoni, F. (2025), Work-Family Trajectories over the Life Course of Japanese Males and Females: A Transition-Oriented Comparison Using Hidden Markov Models. In: Nakai, M. (eds.), Advances in Quantitative Approaches to Sociological Issues. Behaviormetrics: Quantitative Approaches to Human Behavior, Springer, 83–113. ISBN: 978-981-96-7109-0. (preprint)