Pennoni F. (2014). Issues on the estimation of latent variable and latent class models, with applications in the social sciences. Scholars’ Press, Saarbücken. Website.
Bartolucci F., Farcomeni A., Pennoni F. (2013). Latent Markov models for longitudinal data, Chapman and Hall/CRC, Boca Raton.
Pennoni, F. (2025). Guest Editorial: Statistical modelling with latent variables, Statistical Modelling, 1-2, https://journals.sagepub.com/doi/full/10.1177/1471082X251355667
Pennoni, F., Pandolfi, S. Bartolucci, F. (2025). LMest: An R Package for Estimating Generalized Latent Markov Models, The R Journal, 74-101, https://doi.org/10.32614/RJ-2024-036
Nakai Pennoni (2025). 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, 20, 83–113, Springer, Singapore.
Bartolucci, F., Pandolfi, S., Pennoni, F. (2025). On a class of finite mixture models that includes hidden Markov models for longitudinal data and related misspecification tests. Journal of Multivariate Analysis, 1-14, https://doi.org/10.1016/j.jmva.2025.105423
Ranalli, G., Pennoni, F., Bartolucci, F., Mira, A. (2025). When non-response makes estimates from a census a small area estimation problem: the case of the survey on graduates’ employment status in Italy. Advances in Data Analysis and Classification, 19, 515–543 (2025). https://doi.org/10.1007/s11634-025-00630-z
Brusa L., Pennoni F. (2025). Variational inference for estimating dynamic stochastic block models through an evolutionary algorithm. Advances in Data Analysis and Classification, https://doi.org/10. 1007/s11634-025-00634-9.
Bartolucci, F., Pennoni, F. (2024). Comparing Fisher Information in Continuous, Dichotomized, and Discretized Data: A Pedagogical Perspective with Illustrations, Electronic Journal of Applied Statistical Analysis, 572-585.
Bartolucci, F., Pennoni, F. (2024). Book Review: VISSER, I., & SPEEKENBRINK, M. Mixture and Hidden Markov Models with R. Springer, Cham, CH. Psychometrika, https://doi.org/10.1007/s11336-024-09958-5.
Pennoni, F., Bartolucci, F., Pandolfi, S. (2024). Variable selection for hidden Markov models with continuous variables and missing data. Journal of Classification, https://doi.org/10.1007/s00357-023-09457-9
Bartolucci, F., Pennoni, F. (2024). Book Review: VISSER, I., & SPEEKENBRINK, M. (2022). Mixture and Hidden Markov Models with R. Springer, Cham, CH. Psychometrika, https://doi.org/10.1007/s11336-024-09958-5.
Cortese, F., Pennoni, F., Bartolucci, F. (2024). Maximum likelihood estimation of multivariate regime switching Student-t copula models. International Statistical Review. https://doi.org/10.1111/insr.12562
Brusa, L., Pennoni, F., Bartolucci, F. (2024). Maximum likelihood for discrete latent variable models via evolutionary algorithms. Statistics and Computing, 34, 62. https://doi.org/10.1007/s11222-023-10358-5
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. https://doi.org/10.1007/s11205-023-03261-z
Sherratt, K., et al. (2023). Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations, eLife. https://elifesciences.org/articles/81916.
Bartolucci, F., Pennoni, F., Vittadini, G. (2023). A Causal Latent Transition Model With Multivariate Outcomes and Unobserved Heterogeneity: Application to Human Capital Development. Journal of Educational and Behavioral Statistics, 48, 387-419.
Pandolfi, S., Bartolucci, F., Pennoni, F. (2023). A hidden Markov model for continuous longitudinal data with missing responses and dropout. Biometrical Journal. https://doi.org/10.1002/bilm.202200016.
Brusa, L., Bartolucci, F., Pennoni, F. (2022). Tempered expectation-maximization algorithm for the estimation of discrete latent variable models, Computational Statistics, 38, 1391–1424, https://doi.org/10.1007/s00180-022-01276-7.
Pennoni, F. Bal-Domńska, B. (2021). NEETs and youth unemployment: A longitudinal comparison across European countries. Social Indicator Research, 162, 739–761.
Bartolucci, F., Pandolfi, S., Pennoni, F. (2022). Discrete latent variable models. Annual Review of Statistics, 9, 425-452 https://doi.org/10.1146/annurev-statistics-040220-091910
Pennoni, F., Bartolucci, F., Forte, G., Ametrano, F. (2021). Exploring the dependencies among main cryptocurrency log-returns: A hidden Markov model, 1-27 Economics Notes. https://doi.org/10.1111/ecno.12193
Bassi, F., Pennoni, F., Rossetto, L. (2021). Market segmentation and dynamic analysis of sparkling wine purchases in Italy, 1-33 Journal of Wine Economics. https://doi.org/10.1017/jwe.2021.20
Bartolucci, F., Pennoni, F., Mira, A. (2021). A multivariate statistical model to predict COVID-19 count data with epidemiological interpretation and uncertainty quantification. Statistics in Medicine, 40, 5351-5372. https://doi.org/10.1002/sim.9129.
Gemma, M., Pennoni, F., Tritto, R., Agostoni, M. (2021). Risk of adverse events in gastrointestinal endoscopy: Zero-inflated Poisson regression mixture model for count data and multinomial logit model for the type of event. PLoS ONE, 1-16.
Gemma, M., Pennoni, F., Braga, M. (2021). Studying Enhanced Recovery After Surgery (ERAS©) core items in colorectal surgery: A causal model with latent variables. World Journal of Surgery, 45, 928-939.
Bassi, F, Pennoni, F., Rossetto, L. (2020). The Italian market of sparkling wines: Latent variable models for brand positioning, customer loyalty, and transitions across brands' preferences. Agribusiness, 36, 542–567.
Garriga, A., Pennoni, F. (2020). The causal Effects of Parental Divorce and Parental Temporary Separation on Children’s Cognitive Abilities and Psychological Well-being According to Parental Relationship Quality. Social Indicator Research, 1-25, https://doi.org/10.1007/s11205-020-02428-2.
Pennoni, F., Genge, E. (2020). Analysing the course of public trust via hidden Markov models: A focus on the Polish society, Statistical Methods and Applications, 29, 399-425.
Pennoni, F., Nakai, M. (2019). A latent class analysis towards stability and changes in breadwinning patterns among coupled households. Dependence Modeling, 7, 234-246.
Bartolucci, F., Pennoni, F. (2019). Comment on: The class of CUB models: statistical foundations, inferential issues and empirical evidence, Statistical Methods and Applications, 1-3.
Pennoni, F., Barbato, M., Del Zoppo, S. (2017). Latent Markov model with covariates to study unobserved heterogeneity among fertility patterns of couples. Frontiers in Public Health, 5, 1-9.
Bartolucci, F., Pandolfi, S., Pennoni, F. (2017). LMest: An R package for latent Markov models for longitudinal categorical data, 1-38, Journal of Statistical Software, 81, 1-38.
Grilli, L., Pennoni, F., Rampichini, C., Romeo, I. (2016). Exploiting TIMSS and PIRLS combined data: multivariate multilevel modelling of student achievement, The Annals of Applied Statistics, 4, 2405-2426.
Pennoni, F., Romeo I. (2016). Latent Markov and growth mixture models for ordinal individual responses with covariates: A comparison. Statistical Analysis and Data Mining,10, 29-39.
Bartolucci, F., Pennoni, F. Vittadini, G. (2016). Causal latent Markov model for the comparison of multiple treatments in observational longitudinal studies, Journal of Educational and Behavioral Statistics, 41, 146-179.
Bartolucci, F., Farcomeni, A., Pennoni, F. (2014). Latent Markov Models: a review of a general framework for the analysis of longitudinal data with covariates (with discussion), Test, 23, 433-465.
Bartolucci, F., Farcomeni, A., Pennoni, F. (2014). Rejoinder on: Latent Markov Models: a review of a general framework for the analysis of longitudinal data with covariates, Test, 23, 484-496.
Bacci, S., Pandolfi, S., 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.
Bartolucci, F., Bacci, S., Pennoni, F. (2014). Longitudinal analysis of self-reported health status by mixture latent auto-regressive model, Journal of the Royal Statistical Society - Series C, 63, 268-288.
Pennoni, F., Vittadini, G. (2013). Two competing models for ordinal longitudinal data with time-varying latent effects: an application to evaluate hospital efficiency. Quaderni di Statistica, 15, 53-68.
Bartolucci F., Pennoni F., Vittadini G. (2012). Evaluation of the degree effect on the work path by a latent variable causal model, Quaderni di Statistica, 14, 17-20.
Bartolucci, F., Pennoni, F., Vittadini, G. (2011). Assessment of school performance through a multilevel latent Markov Rasch model, Journal of Educational and Behavioral Statistics, 36, 491-522.
Bartolucci, F, Pennoni, F., Francis, B., (2007). A latent Markov model for detecting pattern of criminal activity, Journal of the Royal Statistical Society - Series A, 170, 115-132.
Bartolucci, F., Pennoni, F. (2007). A class of latent Markov models for Capture-Recapture data allowing for Time, Heterogeneity and Behavior effects, Biometrics, 63, 568-578.
Brand, D. A., Saisana, M., Rynn, L. A., Pennoni, F., Lowenfels, A. B. (2007). Comparative analysis of alcohol control policies in 30 countries, PLoS Medicine, 4, 752-759.
Bartolucci, F., Pennoni, F. (2007). On the approximation of the quadratic exponential distribution in a latent variable context, Biometrika, 94, 745-754.
Pennoni, F., Tarantola, S., Latvala, A. (2005). The 2005 European e-Business Readiness Index, EUR Report 22155 EN, European Commission IPSC, Luxembourg, pp. 1-53.
Pennoni, F. (2004). Fitting directed graphical models with one hidden variable, Metodoloski zvezki, (Advances in Methodology and Statistics), 1, 119-130.
Brusa L., Pennoni F. (2025) Stochastic Block Model Based on Variational Inference and its Extensions: An Application to Examine Global Migration Dynamics. In: “Nakai, M. (eds) Advances in Quantitative Approaches to Sociological Issues. Behaviormetrics: Quantitative Approaches to Human Behavior, 20, 1–27, Springer, Singapore”. https://doi.org/10.1007/978-981-96-7109-0_1
Brusa L., Pennoni F., Bartolucci F., Peruilh Bagolini R. (2025). Prediction of early warning crises by a hidden Markov model with covariates. In: Methodological and Applied Statistics and Demography II, SIS 2024, Short Papers, Solicited Sessions, 146–152.
Brusa L., Pennoni F., Bartolucci F., Maggi L. (2025) Dynamic Classification Through Three-Step Estimation: Evidence from a Multinational Longitudinal Study of Myasthenia Gravis Patients. In: Statistics for Innovation II, SIS 2025, Short Papers, Contributed Sessions 1, 270–276.
Bartolucci, F., Greenacre, M., Pandolfi, S., Pennoni, F., (2024). 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.
Brusa, L., Pennoni, F., Bartolucci, F., Bagolini, R. P. (2024). Prediction of Early Warning Crises by a Hidden Markov Model with covariates. Book of short papers 50th Scientific Meeting of the Italian Statistical Society, Università degli Studi di Bari, Bari, June 23-25, 2021. Pearson, pp. 183-188.
Pennoni, F., Bartolucci, F., Vittadini, G. (2023). Latent potential outcomes: An analysis of the effects of programs aimed at improving student’s non-cognitive skills. Statistical Methods for Service Quality Evaluation, 11th International Conference IES 2023, Statistical Evaluation System at 360°, University of Chieti-Pescara, pp. 374-379.
Pennoni, F., Nakai, M. (2023). Exploring Heterogeneity in Happiness: Evidence from a Japanese Longitudinal Survey. Behaviormetrics: Quantitative Approaches to Human Behavior, Facets of Behaviormetrics, pp. 1-30.
Pennoni, F., Bartolucci, F., Vittadini, G. (2023). Latent potential outcomes: An analysis of the effects of programs aimed at improving student’s non-cognitive skills. Statistical Methods for Service Quality Evaluation, 11th International Conference IES 2019, Statistical Evaluation System at 360°, University of Chieti-Pescara, pp. 374-379.
Brusa, L., Pennoni, F., Bartolucci, F. (2023). Evolutionary algorithms for the estimation of discrete latent variable models. Proceeding of the 37th International Workshop on Statistical Modelling (IWSM), 17-21 July 2023, Dorint-Hotel, Dortmund, pp. 1-6.
Brusa, L. Pennoni, F. (2023). Improving node classification in temporal networks through evolutionary algorithms. Book of Short Papers CLADAG 2023 14th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, 11-13 September, University of Salerno, Salerno, IT, Eds. pp. 1-4.
Bartolucci, F., Pennoni, F., Cortese, F., (2021). Hidden Markov and regime switching copula models for state allocation in multiple time-series. Book of abstract and short papers 13th Scientific Meeting of the Classification and Data Analysis Group, Università Studi di Firenze, September 9-11, 2021. Firenze University Press, pp. 36-39.
Brusa, L., Bartolucci, F., Pennoni, F. (2021). A Tempered Expectation-Maximization Algorithm for Latent Class Model Estimation. Book of short papers 50th Scientific Meeting of the Italian Statistical Society, Università Studi di Cagliari, June 23-25, 2021. Pearson, pp. 183-188.
Pennoni, F., Bartolucci, F., Pandolfi, S. (2021). A Hidden Markov Model for Variable Selection with Missing Values. Book of short papers 50th Scientific Meeting of the Italian Statistical Society, Università degli Studi di Cagliari, June 23-25, 2021. pp. 145-150.
Bartolucci, F., Pennoni, F., (2020). Alcuni modelli per dati di conteggio con applicazione a COVID-19. In Il COVID-19 tra emergenza sanitaria ed emergenza economica: riflessioni dal mondo delle scienze sociali. Perugia, Morlacchi Editore, pp. 39-57.
Nakai, M., Pennoni, F. (2020). Identifying groups with different traits using fourteen domains of social consciousness: A multidimensional latent class graded item response theory model. Advanced Researches in Behaviormetrics and Data Science (Springer), Essays in Honor of Akinori Okada, pp. 1-20.
Bacci, S., Bartolucci, F., Pennoni, F. (2020). Multilevel Model-Based Clustering: A New Proposal of Maximum-A-Posteriori Assignment. In Advanced Researches in Classification and Data Science (Springer), I. Tadashi, A. Okada, S. Miyamoto, F. Sakaori, Y. Yamamoto, and M. Vichi Eds. pp. 1-18.
Bassi, F., Pennoni, F., Rossetto L. (2019). The evolution of the purchase behavior of sparkling wines in the Italian markets. Book of Short Papers CLADAG 2019 12th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, 11-13 September, University of Cassino and Lazio Meridionale, Cassino, IT, G. C. Porzio, F. Greselin, S. Balzano Eds. pp. 1-4.
Pennoni, F., Genge, E. (2019). A multivariate hidden Markov model: prospects for the course of public trust in Poland. Proceeding of the 34th International Workshop on Statistical Modelling (IWSM), 7-12 July 2019, University of Guimarães, Portugal, pp. 248-251.
Bartolucci, F., Pennoni, F. Vittadini, G. (2019). Latent variable models for evaluation systems, Statistical Methods for Service Quality Evaluation, 9th International Conference IES 2019, Statistical Evaluation System at 360°, M. Bini, P. Amenta, A. D’Ambra, and I. Camminatiello Eds, Cuzzolin, Napoli, pp. 1-6.
Pennoni, F. and Nakai, M. (2018). A latent variable model for a derived ordinal response accounting for sampling weights, missing values and covariates, Proceedings of the Second international conference on Advances in Statistical Modelling of Ordinal Data, University of Naples, Naples, 24-26 October. pp.155-162.
Bartolucci, F., Cardinali, A., Pennoni, F. (2018). A generalized moving average convergence/divergence for testing semi-strong market efficiency. Eighth International Conference on Mathematical and Statistical methods for Actuarial Sciences and Finance (MAF), 4-6 April, Madrid, Spain, pp. 1-4.
Pennoni, F., Piccarreta, R. (2017). Dynamic sequential analysis of careers. CLADAG 2017 11th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, 13-15 September, University of Milano-Bicocca, Milano, IT, pp. 1-6.
Berta, P., Pennoni, F., Vinciotti. V. (2017). Outcome evaluation in healthcare: the multilevel logistic cluster weighted model. CLADAG 2017 11th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, 13-15 September, University of Milano-Bicocca, Milano, IT, pp. 1-6.
Bartolucci, F., Pandolfi, S., Pennoni, F. (2017). Package LMest for latent Markov analysis of longitudinal categorical data. 11 Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, 13-15 September, University of Milano-Bicocca, Milano, IT, pp. 1-6.
Pennoni, F. (2016). Modelling a multivariate hidden Markov process on survey data. Proceedings of the 48th Scientific Meeting of the Italian Statistical Society, Università degli studi di Salerno, June 8-10, 2016. pp. 1-10.
Bartolucci, F., Francis, B. Pandolfi, S., Pennoni F. (2015). Robust maximum likelihood estimation of latent class models. Proceedings Vol. 1 30th International Workshop on Statistical Modelling (IWSM 2015) Eds. Friedl H. and Wagner H., 6-10 July, Linz, pp. 94 -99.
Pennoni, F. Vittadini, G. (2015). Hidden Markov and mixture panel data models for ordinal variables derived from original continuous responses, Advances in Mathematics and Statistical Sciences, Proceedings of the 3rd International conference on Mathematical, Computational and Statistical Sciences (MCSS) (Mastorakis, N. E., Ding, A., Shitikova M. V. Eds.), 22-24 February, Dubai, pp. 98-106.
Pennoni, F., Vittadini, G. (2014). Stochastic models for ordinal panel data with individual and time-varying latent effects. Recent advances in applied mathematics, modelling and simulation. Proceeding of the 8th International conference on applied Mathematics, Simulation Modelling (ASM’14), 22-24 Novembre, Firenze, pp.123-126.
Grilli, L., Pennoni, F., Rampichini, C., Romeo, I. (2014). Multivariate multilevel modelling of student achievement data, Proceedings of the 47th Scientific meeting of the Italian Statistical Society (SIS), 11-13 June 2014, Cagliari, pp. 1-6.
Pennoni, F., Vittadini, G. (2013). Hospital efficiency under competing panel data models, Book of Abstracts CLADAG 2013, Ninth Scientific Meeting of the Classification and Data Analysis Group (CLADAG), 18-20 Settembre 2013, Modena, pp. 373-376.
Bartolucci, F., Pennoni, F. (2011). Impact evaluation of job training programs by a latent variable model. In: Ingrassia S., Rocci R., Vichi M. (Eds.), New Perspectives in Statistical Modelling and Data Analysis. Springer, pp. 65-73.
Agasisti, T., Pennoni, F., Vittadini, G. (2011). Extending value-added models for educational production: stochastic processes and clustering, Proceeding CLADAG 2011 Classification and Data Analysis, 7-9 Settembre, Pavia, pp. 154-157.
Bartolucci F., Pennoni, F., Vittadini, G. (2010). Assessment of school performance through a multilevel latent Markov Rasch model, Proceeding of the 25st International Workshop on Statistical Modeling, 5-9 July University of Glasgow, UK, pp. 57-62.
Bacci, S., Bartolucci, F., Pennoni, F. (2010). Markov-switching autoregressive latent variable models for longitudinal data, Proceeding of the 25st International Workshop on Statistical Modeling, 5-9 July 2010, University of Glasgow, UK, pp.79-84.
Pennoni, F., Bartolucci, F. (2009). Impact evaluation of job training programs by a latent variable model. Proceedings of the Seventh Meeting of the Classification and Data Analysis Group (CLADAG), 9-11 Settembre, Catania, pp. 53-56.
Bartolucci, F., Farcomeni, A., Pennoni, F. (2009). Analysis of longitudinal data via latent Markov model and its extensions. Proceedings of the Seventh Meeting of the Classification and Data Analysis Group (CLADAG), 9-11 Settembre, 2009, Catania, pp. 375-378.
Bartolucci, F., Pennoni, F., Lupparelli, M. (2008). Likelihood inference for the Latent Markov Rasch model, In: Huber N., Limnios M., Mesbah M., Nikulin M. (Eds.), Mathematical Methods for Survival Analysis, Reliability and Quality of Life, Wiley, pp. 243-257.
Tarantola, S., Pennoni, F. (2005). The e-Business Readiness Index 2005: Robustness Assessment, In: P. Cunninghan, M. Cunninghan (Eds), Innovation and the knowledge Economy: Issue, Applications, Case studies, Amsterdam, Ios, pp. 112-119.
Bartolucci, F., Pennoni, F. (2005). A class of multivariate latent Markov models for clustering patterns of criminal activity, Book of short papers, Fifth Meeting of the Classification and Data Analysis Group (CLADAG), 6-8 Giugno, Parma, pp. 237-240.
Bartolucci, F., Pennoni, F. (2004). A latent Markov model to classifying criminal activity, Proceeding of the 19th International Workshop on Statistical Modeling, 4-8 Luglio, Firenze, pp. 306-309.
Brusa L., Bartolucci F., Pennoni F., Peruilh Bagolini R. (2025). A penalized maximum likelihood estimation for hidden Markov models to address latent state separation. In: 26th International Conference on Computational Statistics (COMPSTAT 2024), August 2024. (p. 27).
Spinelli, D., Pennoni, F., Bartolucci, F., Vittadini, G. (2025). A latent class model for item responses and response times. Innovation and Society – Statistics and Data Science for Evaluation and Quality, University of Padova, Bressanone, 25-27 June.
Pandolfi, S., Bartolucci, F., Pennoni, F. (2025). Hidden Markov models for longitudinal data: advances to deal with missing data, dropout, and variable selection, Workshop: Markov, Semi-Markov Models and Associated Fields (from Theory to Application and back), Paris, 1-4 July.
Spinelli, F., Pennoni, F., Bartolucci, F., Vittadini, G., Spinelli D. (2025). Analysis of Sacco hospital longitudinal data by hidden Markov models. Workshop finale: A latent Markov model for long-COVID symptoms: application to the Sacco hospital data, Department of Economics, University of Perugia, Perugia, 10 February.
Brusa, L., Bartolucci, F., Maggi, L., Pennoni, F. (2024). A multidimensional hidden Markov model for longitudinal item responses, IBC2024 - 32nd International Biometric Conference, Atlanta - United States, 8-13 December.
Brusa, L., Pennoni, F., Bartolucci, F., Peruilh Bagolini, R. (2024). Addressing latent state separation in hidden Markov models for categorical data with covariates: A penalised maximum likelihood approach, CCDA2024 - Challenges for Categorical Data Analysis, London School of Economics- London, United Kingdom, 31 October – 1st November.
Brusa, L., Pennoni, F., Bartolucci, F., Peruilh Bagolini, C. (2024). Prediction of early warning crises by a hidden Markov model with covariates, SIS 2024 - 52nd Scientific Meeting of the Italian Statistical Society, Università degli Studi di Bari Aldo Moro, Bari, 17-20 June.
Brusa, L., Pennoni, F., Bartolucci, F., Peruilh Bagolini, R. (2024). In-sample and out-of-sample forecasts for early warning systems using a hidden Markov model with covariates, 1st Workshop for the PRIN Project “Hidden Markov Models for Early Warning Systems”, University of Perugia, 19 September.
Brusa, L., Bartolucci, F., Pennoni, F., Peruilh Bagolini, R. (2024). A penalized maximum likelihood estimation for hidden Markov models to address latent state separation, COMPSTAT 2024 - 26th International Conference on Computational Statistics, University of Gießen, Germany, 27-30 August.
Nakai, M., Pennoni, F. (2024). Gendered work-family pathways over the life course: A comparison through hidden Markov models. 52nd Annual Meeting of the Behaviormetric Society, Osaka University of Economics, Osaka, 10-13 September.
Pennoni, F. (2024). Impact and developments on social science research of discrete latent variable models, 52nd Annual Meeting of the Behaviormetric Society, Osaka University of Economics, Osaka, 10-13 September.
Pennoni, F. (2024). Latent probability models for cross-sectional and longitudinal data, Latent variable models: Methodological and applicative developments for lifestyle and society, Kickoff meeting of Working Package 6.2 – “Stili di vita e società”, University of Perugia, 2 February 2024. Slides
Pandolfi, S., Bartolucci, F., Pennoni, F. (2023). Maximum likelihood inference for hidden Markov models with parsimonious parametrizations of transition matrices. 16th International Conference of the ERCIM WG on Computational and Methodological Statistics, HTW Berlin, University of Applied Sciences, 16-18 December.
Cortese, F., Bartolucci, F., Pennoni, F. (2023). Maximum likelihood estimation of multivariate regime switching Student-t copula models. Statistics@Naples, University of Naples, Naples, 24 June.
Pennoni, F., Bartolucci, F., Vittadini, G., Spinelli D. (2023). Analysis of Sacco hospital longitudinal data by hidden Markov models. Workshop intermedio: Analisi causale delle determinanti dello stato di salute dei pazienti affetti da “long-Covid” sulla base di dati clinici, funzionali e strumentali: uno studio longitudinale multicentro, Department of Economics, University of Perugia, Perugia, 26 May, 2023.
Brusa, L., Bartolucci, F., Pennoni, F. (2022). Alternative methods for parameter estimation in discrete latent variable models. ERCIM WG on Computational and Methodological Statistics 15th International Conference of the ERCIM WG on Computational and Methodological Statistics, 16th International Conference on Computational and Financial Econometrics, King’s College, London, 17-19 December.
Cortese, F., Bartolucci, F., Pennoni, F. (2022). Maximum likelihood estimation of multivariate regime switching Student-t copula models. ERCIM WG on Computational and Methodological Statistics 15th International Conference of the ERCIM WG on Computational and Methodological Statistics, 16th International Conference on Computational and Financial Econometrics, King’s College, London, 17-19 December.
Pennoni, F., Nakai, M. (2022). Measuring subjective well-being over time: Findings from an hidden Markov model with covariates. European Conference on Data Analysis, University of Naples Federico II, Naples, Italy, 14-16 September.
Bartolucci, F., Pandolfi, S., Pennoni, F. (2022). A Misspecification test for hidden Markov models based on finite mixture models. Computational Statistics. Book of Abstracts 24th International Conference on Computational Statistics (COMPSTAT), University of Bologna, Bologna, 23-26 August.
Bartolucci, F., Pandolfi, S., Pennoni, F. (2022). Misspecification tests for hidden Markov models based on a new class of finite mixture models. Classification and Data Science in the Digital Age. Book of Abstracts 17th conference of the International Federation of Classification Societies, University of Porto, Porto, Portugal, 19-23 July.
Pennoni, F., Bartolucci, F., Pandolfi, S. (2022). Misspecification tests for hidden Markov models based on a new class of finite mixture models. Book of Abstract 17th Conference of the International Federation of Classification Societies (Classification and Data Science in the Digital Age), Faculty of Economics of the University of Porto, Portugal, 19-23, July, p. 271.
Pennoni, F. (2022). Hidden Markov models: theory, applications and new perspectives. Book of Abstracts Challenges for Categorical Data Analysis (CCDA 2022), University of Perugia, Perugia, Italy, 12-13, May, p. 6.
Cortese, F., Bartolucci, F., Pennoni, F. (2022). A Regime switching Student-t copula model for the analysis of cryptocurrencies data. Book of Abstracts Mathematical and Statistical Methods for Actuarial Sciences and Finance (MAF), University of Salerno, Salerno, Italy, 20-22 April, p. 68.
Pennoni, F., Bartolucci, F., Pandolfi, S. (2022). Maximum likelihood estimation of Hidden Markov models for continuous longitudinal data with missing responses and dropout. Book of Abstracts 11th Conference of the Asian Regional Section of the International Association for Statistical Computing (IASC-ARS), Doshisha University, Kyoto, Japan, 21-24 February, pp. 22-23.
Brusa, L., Bartolucci, F., Pennoni, F. (2022). Tempered Expectation-Maximization algorithm for discrete latent variable models. Book of Abstracts 11th Conference of the Asian Regional Section of the International Association for Statistical Computing (IASC-ARS), Doshisha University, Kyoto, Japan, 21-24 February, pp. 21-22.
Pennoni, F., Bartolucci, F., Pandolfi, S. (2021). Variable selection in hidden Markov models with missing data. Book of Abstracts 14th International Conference of the ERCIM WG on Computational and Methodological Statistics, 15th International Conference on Computational and Financial Econometrics, King’s College London, 18-20 December. pp. 41- 42.
Pennoni, F., Bartolucci, F., Mira, A. (2021). A multivariate statistical approach to predict COVID-19 count data with epidemiological interpretation and uncertainty quantification, World Meeting of the International Society for Bayesian Analysis, Session: Bayesian Methods for Biomedical Research, 28 Giugno-2 Luglio. https://events.stat.uconn.edu/ISBA2021/programs.html
Pennoni, F., Bal-Domańska, B. (2021). Hidden Markov model to analyze NEET and youth unemployment rates comparing EU countries over time. Applied Stochastic Models and Data Analysis International Conference, 1-4 June, Athens, Greece.
Pennoni, F., Bartolucci, F., Favaro, D., Sciulli, D. (2021). Streaming and peer effects on the development of social capital: An analysis based on a multivariate causal hidden Markov model. Ninth Italian Congress of Econometrics and Empirical Economics, 21-23 January, University of Cagliari.
Pennoni, F., Bartolucci, F., Forte, G., Ametrano, F. (2020). Multivariate hidden Markov model for cryptocurrency. Crypto Asset Lab Conference, 27 October, University of Milano-Bicocca.
Bartolucci, F., Pandolfi, S., Pennoni, F., Serafini, A. (2020). Diversity in socio-economic growth at country level: a multivariate hidden Markov model. Book of Abstract of the International Conference on Distributions and Inequality Measures in Economics, 20-21, February, University of Milano-Bicocca, Italy pp. 74-75.
Pennoni, F., Nakai, M. (2019). Assessment of recent social attitudes in Japan: A latent class item response theory model for web survey data, 16th Conference of the International Federation of Classification Societies, 26-29 August, Thessaloniki, Greece.
Pennoni, F., Bartolucci, F., Serafini, A., Pandolfi, S. (2019). Hidden Markov models for continuous multivariate data with missing responses. 16th Conference of the International Federation of Classification Societies, 26-29 August, Thessaloniki, Greece.
Bartolucci, F., Pennoni, F., Vittadini, G. (2019). Latent variable models for the evaluation systems. Statistical evaluation systems at 360°: techniques, technologies and new frontiers. 9Th International conference organized by the statistics for the evaluation and quality in Services Group of the Italian Statistical Society. European University of Rome, 4-5 July, 2019.
Pennoni, F., Garriga, A. (2019). Effects of family disruption according to parental relationship quality on children's school readiness. In Book of abstract of the LVI Riunione Scientifica della Società Italiana di Economia Demografia e Statistica, Benessere e Territorio: Metodi e Strategie, Ascoli Piceno, 23-24 May, p.1.
Pennoni, F., Genge, E. (2018). Predicting trends of institutional confidence through a hidden Markov model with survey weights and missing responses, ERCIM WG on Computational and Methodological Statistics 11th International Conference of the ERCIM WG on Computational and Methodological Statistics, 12th International Conference on Computational and Financial Econometrics, University of Pisa, 14-16 December. p. 65.
Pennoni, F., Paas, L., Bartolucci, F. (2018). Causality patterns of a marketing campaign conducted over time: evidence from the latent Markov model. 49th Meeting of the Italian Statistical Society, 20-22 June, Palermo, Italy.
Pennoni, F., Paas, L., Bartolucci, F. (2018). Causal effects of dynamic direct mail campaigns on customer product portfolios. 47th European Marketing Academy Annual Conference (EMAC), May 29-June 1, University of Strathclyde, Glasgow, UK, p. 1.
Genge, E., Pennoni, F. (2018). A hidden Markov approach to reflect on the recent course of trust in the public and financial institutions of the Polish society. First Italian Workshop of Econometrics and Empirical Economics (IWEEE): Panel Data Models and Applications, 26-27 January, Milano Italy.
Pennoni, F. (2017). A review of panel data models with a Markov dependent structure for univariate nd multivariate ordinal responses. 11th International Conference of the ERCIM WG on Computational and Methodological Statistics and 10th International Conference on Computational and Financial Econometrics (ERCIM), 16-17 December, London, UK, p. 61.
Bacci, S., Bartolucci, F., Pennoni, F. (2017). Optimal model-based clustering with multilevel data. In Book of abstracts: the challenge of data science in the era of Big data, 15th Conference of the International Federation of Classification Societies, 8-10 August, Tokai University, Tokyo, Japan, Dloadabs, Slides.
Bartolucci, F., Pandolfi, S., Pennoni, F. (2017). Optimal model-based clustering with multilevel data. In Book of abstracts: the challenge of data science in the era of Big data, Conference of the international federation of classification societies, 8-10 August, Tokai University, Tokyo, Japan, Dloadabs, Slides
Pennoni F., Garriga, A., Romeo, I. (2017). Conditional average treatment effect: an application related to the partner union quality and divorce on the child’s psychological wellbeing, 3rd Meeting Futuro in ricerca FIRB, 1-2 February, Bologna.
Pennoni, F., Romeo, I. (2017). A comparison between two statistical models to analyse and predict individual changes over time, 3rd Meeting Futuro in ricerca FIRB, 1-2 February, Bologna, Italy.
Pennoni, F., Grilli, L., Rampichini, C., Romeo, I. (2017). A multivariate multilevel model to analyze educational achievement in Reading, Mathematics and Science in Italy, 3rd Meeting Futuro in ricerca FIRB, 1-2 February, Bologna, Italy.
Pennoni, F., Elisei, G. (2015). A discrete-valued latent stochastic process for the estimation of credit migration matrices. 8th International Conference of the ERCIM WG on Computational and Methodological Statistics and 9th International Conference on Computational and Financial Econometrics (ERCIM), 12-14 December, Londra, UK p.39.
Pennoni, F., Bartolucci, F., Baccarrelli, A. Colicio, E., Vittadini, G. (2015). Exploring the dependencies between epigenetic pathways and air pollution with the use of the latent Markov model. 4th International Conference and Exhibition on Biometrics & Biostatistics, 16-18 November, San Antonio, USA, 4, p. 38.
Pennoni, F., Romeo, I. (2015). Latent Markov and Growth Mixture models: a comparison. 10th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG), 8-10 October, Santa Margherita di Pula, IT, pp. 181-184.
Pennoni, F., Romeo, I., Barbato, M., Del Zoppo, S. (2015). Effectiveness of the natural family planning method by considering unobserved heterogeneity. World Congress Feed life, Nourish love and Sustain the family, 11-14 June, 2015, Milano.
Garriga, A., Pennoni, F. (2015). Parents’relationship quality, parental divorce and children’s well-being, FIRB- Futuro in ricerca- Meeting, 23-24 Gennaio, Roma.
Grilli, L., Pennoni, F., Rampichini, C., Romeo, I. (2015). Exploiting TIMSS & PIRLS combined data: multivariate multilevel modelling of student achievement, FIRB- Futuro in ricerca- Meeting, 23-24 January, Sapienza Università di Roma, Roma.
Bartolucci, F., Pennoni, F., Vittadini, G. (2015). A latent Markov model from a new perspective with an application, FIRB- Futuro in ricerca- Meeting, 23-24 Gennaio, Sapienza Università di Roma, Roma.
Francis, B. Pennoni, F. Pandolfi S. Bartolucci F. (2014). Robust latent class analysis through outlier detection and modelling. Book of abstracts 7th International conference of the CFE-ERCIM working group on Computational and methodological Statistics, 5-8 Dicembre, 2014, London.
Bartolucci, F., Pennoni, F., Romeo, I. (2014). A comparison between the latent Markov and growth mixture models for the analysis of longitudinal data, MBC2 Workshop on Model-Based Clustering and Classification, 3-5 Settembre 2014, Catania. Poster session.
Pennoni, F. (2014). An overview of the Latent Markov model. Statistics with UNObservable VAriables, Statistical models for HumAn Perception and Evaluation (SUNOVA & SHAPE), 21 Ottobre, Brescia.
Francis, B, Pennoni, F. (2014). Improving latent class analysis through outlier detection– an example from criminal careers research. Workshop on model based clustering and classification MBC2, 3-5 Settembre, Catania.
Grilli, L., Pennoni, F., Rampichini, C., Romeo, I. (2014). Multivariate multilevel model for the analysis of PIRLS & TIMMS data, Proceedings of the VI European Congress of Methodology, 23-25 Luglio 2014, Utrecht.
Bartolucci, F., Pennoni, F., Vittadini, G. (2013). Causal effect of the degree programs on the work path of the graduates in the multivariate latent Markov model, Proceeding of the 7th International Conference on Computational and Financial Econometrics (ERCIM), 14-16 Dicembre 2013, Londra, p. 166.
Pennoni, F., Vittadini, G. (2013). Two competing models for ordinal longitudinal data: an application to evaluate hospital efficiency, Advances in Statistical Modelling of Ordinal data (ASMOD), 25-26 Novembre 2013, Napoli.
Pennoni, F. (2013). Studying employment pathways of graduates by a latent Markov model, In Brentari, E., Carpita, M. (Eds), Atti della Riunione Scientifica della Società Italiana di Statistica (SIS) in Advances in latent variables, methods, models and applications, Vita e Pensiero, 19-21 Giugno, Brescia, pp. 1-6.
Bartolucci, F., Bacci, S., Pandolfi, S., Pennoni, F. (2012). A comparison of some criteria for states selection of the latent Markov model for longitudinal data, Workshop on Model Based Clustering and Classification MBC2, 6-7 Settembre, 2012, Catania.
Bartolucci, F., Bacci, S., Pennoni, F. (2011). Mixture latent autoregressive models for longitudinal data. Conference honouring the work of Professor D. J. Bartholomew, 12-13 December, 2011, The London school of Economics and Political Science, UK.
Bartolucci, F., Pennoni, F., Vittadini, G. (2011). Latent Markov models from a potential outcome prospective for causal inference in dynamic settings, Innovation and Society - Statistical methods for service evaluation 2011, 30 Maggio-1 Giugno, Firenze, p. 74.
Bartolucci, F., Pennoni, F., Pieroni, L. (2010). A latent class version of the inverse probability-to-treatment weighted estimator for dynamic causal effects. Proceedings of the Joint meeting of the German Classification Society and the Classification and Data Analysis Group of the Italian Statistical Society (GfKl – CLADAG), 8-10 Settembre, Firenze, pp. 207-208.
Bartolucci, F., Pennoni, F. (2008). The latent Markov Rasch model. 73rd Annual Meeting of the Psychometric Society, New Hampshire, Durham (USA), Luglio 2008, p. 23.
Bartolucci, F., Pennoni, F. (2004). Transition in criminal careers: a hidden Markov approach. Proceedings of the 2004 International Conference of the Royal Statistical Society: Connecting practice with research, 7-10 Settembre, Manchester, UK, pp. 39-40.
Pennoni, F. (2004). On the estimation of path analysis models with hidden variables, Atti della XLII Riunione Scientifica della Società Italiana di Statistica (SIS), 9-11 Giugno, Bari, pp. 621-624.
Pennoni, F. (2004). On the estimation of directed acyclic graph models with one hidden variable, Proceedings of the Sixth International conference on Social Science Methodology, 16-20 Agosto, Amsterdam, p. 311.
Francis, B., Pennoni, F., Shoothill, K. (2004). A local likelihood approach to Classifying Criminal Activity, Proceedings of the 41st Academy of Criminal Justice service, 9-13 March, Las Vegas, Nevada.
Francis, B., Pennoni, F. (2003). Classifying Criminal Activity: a latent class approach to longitudinal event data, Proceedings of the 54th Session of the International Statistical Institute, 13-20 Agosto, Berlino, pp. 353-354.
Marchetti, M., Pennoni, F., Stanghellini, E. (2003). Fitting Gaussian DAG Models with Hidden Variables in R, Presentation to the workshop on Computational Aspects of Graphical Models in R, Aalborg (DK).
Pennoni, F. (2003). Research hypothesis on the latent structure of data in the social sciences through conditional independence models, Proceedings of the Annual Research Students’ Conference in Probability and Statistics, Surrey University (UK), p. 46.
Pennoni, F., Rutigliano, I. (2021). Come cambierà la fiducia nelle istituzioni? Narrazione scientifica, Monnalisa Bytes. https://monnalisabytes.com/web-stories/fiducia-istituzioni/
Pennoni, F., Rutigliano, I. (2021). How is trust changing in institutions? Scientific Storytelling, Monnalisa Bytes. https://monnalisabytes.com/web-stories/en-trust-institution/
Bartolucci, F., Pennoni, F., Mira, A. (2020). Modelli univariati e multivariati per serie storiche di conteggi con applicazione a COVID-19, Statistica e Società, Anno IX Edizione Speciale Covid-19, pp.1-2.
Pennoni, F., Genge, E. (2019). Analysing the course of trust towards public and financial institutions via Hidden Markov Models. (http://ssrn.com/abstract=3355798)
Pennoni, F., Paas, L., Bartolucci, F. (2018). Inverse-Probability-of-Treatment Weighting for Endogeneity Correction: A Hidden Markov Model for Assessing Effects of Multiple Direct Mail Campaigns (Sottoposto per la pubblicazione alla rivista Journal of Interactive Marketing) (https://ssrn.com/abstract=3281156).
Nakai, M., Pennoni, F. (2018). A latent class analysis towards stability and changes in breadwinning patterns among coupled households. (https://mpra.ub.uni-muenchen.de/89950/).
Garriga, A., Pennoni, F., (2017). The influence of parental divorce, parental temporary separation and parental relationship quality on children’s school readiness. (https://mpra.ub.uni-muenchen.de/82892/).
Pennoni, F. Romeo, I. (2016). Latent Markov and growth mixture models for ordinal individual responses with covariates: a comparison. (https://mpra.ub.uni-muenchen.de/72939/).
Bartolucci, F. Pennoni, F. Vittadini, G. (2015). Causal latent Markov model for the comparison of multiple treatments in observational longitudinal studies. (https://mpra.ub.uni-muenchen.de/66492/).
Grilli, L., Pennoni, F., Rampichini, C., Romeo, I. (2015). Exploiting TIMSS and PIRLS combined data: multivariate multilevel modelling of student achievement, (http://arxiv.org/abs/1409.2642).
Bartolucci, F., Farcomeni, A, Pandolfi, S., Pennoni, F. (2015). LMest: an R package for latent Markov models for categorical longitudinal data. (http://arxiv.org/abs/1501.04448).
Grilli, L., Pennoni, F., Rampichini, C., Romeo, I. (2014). Exploiting TIMSS and PIRLS combined data: multivariate multilevel modelling of student achievement, (http://arxiv.org/abs/1409.2642).
Grilli, L., Pennoni, F., Rampichini, C., Romeo, I. (2014). Un focus sui dati italiani dell’indagine TIMSS&PIRLS 2011, Statistica e Società, 2, 14-16.
Bartolucci, F., Farcomeni, A., Pennoni, F. (2012). A note on the application of Oakes' identity to obtain the observed information matrix of hidden Markov models. (http://arxiv.org/abs/1201.5990).
Bartolucci, F., Farcomeni, A., Pennoni, F. (2010). An overview of the latent Markov models for longitudinal data. (http://arxiv.org/abs/1003.2804).
Pennoni, F., Bartolucci, F. (2010). Valutazione delle azioni di politica attiva basate sulla dote lavoro di Regione Lombardia, Sperimentazione valutatore indipendente 2008-2010. Codice IReR: 2008B076. Istituto Regionale di Ricerca della Lombardia, pp. 172-195.
Pennoni, F. (2010). Proposta metodologica, in CRISP- Centro di Ricerca interuniveristario per i servizi di pubblica utilità (Eds.), Valutazione delle azioni di politica attiva basate sulla dote lavoro di Regione Lombardia, pp. 14-22.
European lifelong learning index (ELLI): Monitoring Lifelong Learning and its effects on economic prosperity and social well-being in the European and regional context. Feasibility Study, Final Report: December 2007. http://www.bertelsmann-stiftung.de/cps/rde/xbcr/SID-0A000F0A-74522695/bst_engl/ELLI-Feasabilitystudy-final_080213.pdf
Bartolucci, F., Pandolfi, S., Pennoni, F., Serafini, A. (2020). Introduction to LMest, Vignette, https://cran.r-project.org/web/packages/LMest/vignettes/vignetteLMest.html
Pennoni, F. Bartolucci, F. (2018). Web resources for the book on Latent Markov models. https://sites.google.com/site/latentmarkovbook/home
Web resources in Wikipedia on latent Markov model (2018)
https://en.wikipedia.org/wiki/Hidden_Markov_model
GitHub page: https://github.com/penful
Brusa, L., Pennoni, F. (2024) R package: dynSBevo “Evolutionary-based estimation of the dynamic stochastic block model”. Available at: https://github.com/LB1304/dynSBevo.
Brusa, L., Pennoni, F., Bartolucci F. (2023) R package: estDLVM “Discrete latent variable model estimation”. Available at: https://github.com/LB1304/estDLVM.
Bartolucci, F., Pandolfi, S., Pennoni, F. (2024). R code HM-FM “On a class of finite mixture models that includes hidden markov models for longitudinal data and related misspecification tests”. Available at: https://github.com/Silvia-Pand/HM-FM
Pandolfi, S., Bartolucci, F., Pennoni, F. (2022). R code HMContMiss “Maximum likelihood estimation of hidden Markov models for continuous longitudinal data with missing responses and dropout”. Available at: https://github.com/Silvia-Pand/HMContMiss