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