Publications
Journal Articles
Pennoni, F., Bartolucci, F., and Pandolfi, S. (2024). Variable Selection for Hidden Markov Models with Continuous Variables and Missing Data, Journal of Classification, in press
Tancini, D., Bartolucci, F., and Pandolfi, S. (2023). A comparison between marginal likelihood and data augmented MCMC algorithms for Gaussian hidden Markov models, Journal of Statistical Computation and Simulation, doi: 10.1080/00949655.2023.2294098
Pandolfi, S., Bartolucci, F., and Pennoni, F. (2023). A hidden Markov model for continuous longitudinal data with missing responses and dropout, Biometrical Journal, 65, 2200016, https://doi.org/10.1002/bimj.202200016
Marino, M.F. and Pandolfi, S. (2022). Hybrid maximum likelihood inference for stochastic block models, Computational Statistics & Data Analysis, 171, 107449, https://doi.org/10.1016/j.csda.2022.107449
Bartolucci, F., Pandolfi, S., and Pennoni, F. (2022). Discrete latent variable models, Annual Review of Statistics and its Application, 9, 425-452, https://doi.org/10.1146/annurev-statistics-040220-091910
Aristei, D., Bacci, S., Bartolucci, F., Pandolfi, S. (2020). A bivariate finite mixture growth model with selection. Advanced in Data Analysis and Classification, in press, doi: 10.1007/s11634-020-00433-4
Bartolucci F., Pandolfi S. (2020). An exact algorithm for time-dependent variational inference for the dynamic stochastic block model. Pattern Recognition Letters, 138, 362-369, ISSN: 0167-8655, doi: 10.1016/j.patrec.2020.07.014
Bartolucci, F., Marino, M.F., Pandolfi, S. (2108). Dealing with reciprocity in dynamic stochastic block models, Computational Statistics and Data Analysis, 123, 86-100, https://doi.org/10.1016/j.csda.2018.01.010
Montanari, G.E., Pandolfi, S. (2018). Evaluation of long-term health care services through a latent Markov model with covariates. Statistical Methods and Applications, 27, 151-173, https://doi.org/10.1007/s10260-017-0390-2
Bartolucci, F., Montanari, G. E. and Pandolfi, S. (2018). Latent ignorability and item selection for nursing home case-mix evaluation, Journal of Classification, 35, 172-193, https://doi.org/ 10.1007/s00357-017-9227-9
Bacci, S., Bartolucci, F., and Pandolfi, S. (2018). A joint model for longitudinal and survival data based on an AR(1) latent process, Statistical Methods in Medical Research, 27, 1285-1311, https://doi.org/10.1177/0962280216659895
Bartolucci, F., Pandolfi, S., and Pennoni, F. (2017). LMest: An R Package for Latent Markov Models for Longitudinal Categorical Data, Journal of Statistical Software, 81, 1-38, doi:10.18637/jss.v081.i04, previous technical report available at http://arxiv.org/abs/1501.04448
Bartolucci, F., Montanari, G.E. and Pandolfi, S. (2016). Item selection by latent class-based methods: an application to nursing home evaluation, Advances in Data Analysis and Classification, 10, 245–262
Bartolucci, F., Montanari, G.E. and Pandolfi, S. (2015). Three-step estimation of latent Markov models with covariates, Computational Statistics and Data Analysis, 83, 287-301
Bacci, S., Pandolfi, S., and Pennoni, F. (2014). A comparison of some criteria for states selection in the latent Markov model for longitudinal data, Advances in Data Analysis and Classification, 8, 125-145
Pandolfi, S., Bartolucci, F. and Friel, N. (2014). A generalized Multiple-try Metropolis version of the Reversible Jump algorithm, Computational Statistics & Data Analysis, 72, 298-314
Bartolucci, F., and Pandolfi, S. (2014), Comment on the paper "On the memory complexity of the forward-backward algorithm," Pattern Recognition Letters, 38, 15-19
Bartolucci, F. and Pandolfi, S. (2014). A new constant memory recursion for hidden Markov models, Journal of Computational Biology, 21, 99-117
Bartolucci, F., Montanari, G.E. and Pandolfi, S. (2012). Dimensionality of the latent structure and item selection via latent class multidimensional IRT models. Psychometrika, 77, 782-802
Pandolfi, S., Bartolucci, F. and Friel, N. (2010). A generalization of the Multiple-try Metropolis algorithm for Bayesian estimation and model selection. Journal of Machine Learning Research: W&CP, 9, 581-588
Rocchi, P., Pandolfi, S. and Rocchi, L. (2010). Classical and Bayesian statistics: a survey upon the dualist production. International Journal of Pure and Applied Mathematics, 58, 255-280