Publications

A more detailed list can be found at this Link

Libri (Books):

• Pennoni, F. (2014). Issues on the estimation of latent variable and latent class models. Scholars'Press.Saarbucken. Flyer

• Bartolucci F., Farcomeni A., Pennoni F. (2013). Latent Markov models for longitudinal data, Chapman and Hall/CRC, Boca Raton, ISBN 9781439817087. Page devoted to LM models.

Articoli in Riviste Scientifiche (Articles)

• 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.

• Pennoni F., Vittadini G. (2013). Two competing models for ordinal longitudinal data with time-varying latent effects: an application to evaluate hospital efficiency. QdS, Journal of methodological and applied statistics, 15, 53-68.

• 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, doi: 10.1007/s11634-013-0154-2. (Previous technical report, http://arxiv.org/abs/1212.0352).

• Bartolucci F., Bacci S., Pennoni F. (2014). Longitudinal analysis of self-reported health status by mixture latent autoregressive model. Journal of the Royal Statistical Society - Series C, doi: 10.1111/rssc.12030 (Previous technical report, http://arxiv.org/abs/1108.1498 ). See also https://link.growkudos.com/1mj4aslo7b4

• 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.

• Pennoni et al. (2012). A note on the application of Oakes' identity to obtain the observed information matrix of hidden Markov models. (Submitted, http://arxiv.org/abs/1201.5990).

• Bartolucci F., Pennoni F. and 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. (2011). Impact evaluation of job training programs by a latent variable model. In: Ingrassia S., Rocci R., Vichi M. (Editors), New Perspectives in Statistical Modelling and Data Analysis, Springer, pp 65-73.

• Pennoni et al. (2010). An overview of latent Markov models for longitudinal data. (Submitted, http://arxiv.org/abs/1003.2804).

• Bartolucci F., Pennoni F. and Lupparelli M. (2008). Likelihood inference for the latent Markov Rasch model, in C. Huber, N. Limnios, M. Mesbah, M. Nikulin (Eds.), Mathematical Methods for Survival Analysis, Reliability and Quality of Life, Wiley, pp. 239-254.

• Bartolucci F., Pennoni F. and 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). An extended class of latent Markov models for capture-recapture data allowing for heterogeneity and behavioral effects, Biometrics, 63, 568-578.

• Brand D. A., Saisana M., Rynn B. S., Pennoni F. and Lowenfels M. D. (2007). Comparative analysis of alcohol control policies in 30 countries, PLoS Medicine Vol. 4, e151 doi:10.1371/journal.pmed.0040151, pp. 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. and Latvala A. (2005). The 2005 European e-Business Readiness Index, EUR Report 22155 EN, European Commission IPSC, Luxembourg, pp. 1-53.

• Tarantola S. and Pennoni F. (2005). The eBusiness Readiness Index 2005: Robustness Assessment, In: P. Cunningham and M. Cunningham (Eds.), Innovation and the Knowledge Economy: Issues, Applications, Case Studies: 2 (Information and Communication Technologies and the Knowledge Economy), Ios, pp. 112-119.

• Pennoni F. (2004). Fitting directed graphical models with one hidden variable, Advances in Methodology and Statistics (Metodoloski zvezki), 1, 119-130.

Atti di Convegni (Conference Proceedings):

• Francis B.,Pennoni F., Pandolfi S., Bartolucci F. (2014). Robust latent class analysis through outlier detection and modelling. Book of Abstract 7th International Conference of the ERCIM Working Group on Computational and Methodological Statistics, 6-8 December 2014, Pisa, p. 46.

• 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. Proceedings of the 8th International on Applied Mathematics, Simulation, Modelling (ASM '14), pp.123-126.

• Pennoni F. (2014). An overview of the latent Markov models. Workshop SUNOVA & SHAPE 2014 - Statistics with UNObservable VAriables, Statistical models for HumAn Perception and Evaluation, Brescia, 21 October, Italy.

• Francis B., Pennoni F. (2014). Improving latent class analysis through outlier detection - an example from criminal research. MBC^2 Workshop on model based clustering and classification, 3-5 September, Catania, Italy.

• Bartolucci F., Pennoni F., Romeo I. (2014). A comparison between the latent Markov and growth mixture models for longitudinal data. MBC^2 Workshop on model based clustering and classification, 3-5 September, Catania, Italy.

• 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 July 2014, Utrecht.

• Grilli L., Pennoni F., Rampichini C., Romeo I. (2014). Multivariate multilevel modelling of student achievement data, Proceedings SIS 2014 47th Scientific meeting of the Italian Statistical Society, 11-13 June 2014, Cagliari, Italy, pp. 1-6.

• Bartolucci F., Pennoni F. and Vittadini G. (2013). Causal effect of the degree programs on the work path of the graduates in the multivatiate latent Markov model, Proceeding 7th, ERCIM 2013, International Conference on Computational and Financial Econometrics, 14-16 Decembre 2013, London, p. 166.

• Pennoni F. and Vittadini G. (2013). Hospital efficiency under competing panel data models, Proceeding CLADAG 2013, Scientific Meeting of the Classification and Data Analysis Group, 18-20 Settembre 2013, Modena, pp. 373-376.

• Pennoni F. (2013). Studying employment pathways of graduates by a latent Markov model, in Brentari, E., Carpita, M. (Eds), 'Advances in latent variables', Vita e Pensiero, Italy, pp. 1-6.

• Bartolucci F., Bacci S., Pandolfi S. and Pennoni F. (2012). A comparison of some criteria for states selection of the latent Markov model for longitudinal data, MBC2-Workshop on model based Clustering and Classification, 6-7 Settembre 2012, Catania.

• Bartolucci F., Bacci S. and Pennoni F. (2011). Mixture latent autoregressive models for longitudinal data. Conference honouring the work of Professor D. J. Bartholomew, 12-13 Dicembre 2011, The London school of Economics and Political Science, UK.

• Agasisti T., Pennoni F. and Vittadini G. (2011). Extending value-added models for educational production: stochastic processes and clustering, Proceeding CLADAG 2011 Classification and Data Analysis, 7-9 Settembre 2011, Pavia, pp. 154-157.

• Bartolucci F., Pennoni F. and Vittadini G. (2011). Latent Markov models from a potential outcome prospective for causal inference in dynamic settings, Proceeding IES - Innovation and Society 2011, 30 Maggio-1 Giugno, Firenze, p. 734.

• Bartolucci F., Pennoni F. and Pieroni L. (2010). A latent class version of the inverse probability-to-treatment weighted estimator for dynamic causal effects, Proceeding GfKl - CLADAG 2010, 8-10 Settembre, Firenze, pp. 207-208.

• Bartolucci F., Pennoni F. and Vittadini G. (2010). Assessment of school performance through a multilevel latent Markov Rasch model. Proceeding IWSM 2010, 5-9 Luglio 2010 University of Glasgow, UK, pp. 57-62.

• Bacci S., Bartolucci F. and Pennoni F. (2010). Markov-switching autoregressive latent variable models for longitudinal data, Proceeding IWSM 2010, 5-9 Luglio 2010 University of Glasgow, UK, pp. 57-62.

• Pennoni, F. and Bartolucci, F. (2009). Impact evaluation of job training programs by a latent variable model, Proceedings CLADAG 2009 Classification and Data Analysis, 9-11 Settembre 2009, Catania, pp. 53-56.

• Bartolucci F., Farcomeni A. and Pennoni, F. (2009). Analysis of longitudinal data via latent Markov model and its extensions. Proceedings CLADAG 2009 Classification and Data Analysis, 9-11 Settembre 2009, Catania, pp. 375-378.

• Bartolucci F. and Pennoni, F. (2006). A quadratic exponential model for the analysis of item response data, Atti della XLII Riunione Scientifica SIS, full contributed paper, 14-16 Giugno, Torino, pp.525-528.

• Bartolucci F. and Pennoni, F. (2005). Modelling behavioral response in capture-recapture studies through latent Markov chains, S.Co. 2005- Modelli complessi e metodi computazionali intensive per la stima e la previsione, full contributed paper, 15-17 Settembre, Brixen, pp. 85-90.

• Bartolucci F. and Pennoni F. (2005). A class of multivariate latent Markov models for clustering patterns of criminal activity, CLADAG 2005 - Classification and Data Analysis, 6-8 Giugno 2005, Parma, pp. 237-240.

• Bartolucci F. and Pennoni F. (2004). Transition in criminal careers: a hidden Markov approach, Proceedings of the Royal Statistical Society International Conference: Connecting practise 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 SIS, Bari, full contributed paper, 9-11 Giugno, Bari, pp. 621-624.

• Bartolucci F. and Pennoni F. (2004). A latent Markov model to classifying criminal activity, Proceeding of the 19th International Workshop on Statistical Modeling, pp. 306-309, Firenze (4-8 Luglio).

• 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. and Shoothill K. (2004). A local likelihood approach to Classifying Criminal Activity,Proceedings of the 41st Academy of Criminal Justice service. 9-13 Marzo, Las Vegas, Nevada.

• Francis B. and Pennoni F. (2003). Classifying Criminal Activity: a latent class approach to longitudinal event data, Proceedings of the 54th Session ISI, full contributed paper, 13-20 Agosto, Berlino, pp. 353-354.

• Marchetti M., Pennoni F. and 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 Student Conference. Surrey University (UK); pp. 15-16.

Rapporti di ricerca (Research Reports):

Grilli L., Pennoni F., Rampichini C., Romeo I. (2014). Exploiting TIMSS and PIRLS combined data: multivariate multilevel modelling of student achievement (Submitted, http://arxiv.org/abs/1409.2642).

Bartolucci F., Pennoni F., Vittadini G. (2013). Causal inference for the latent Markov model: an application to the study of the effect of the univerisity degree on the work path. (Submitted).

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.

Pennoni F. (2010). Proposta metodologica, in CRISP- Centro di Ricerca interuniveristario per i servizi di pubblica utilita' (Eds.), Valutazione delle azioni di Politica attiva basate sulla dote lavoro di Regione Lombardia, pp. 14-22.

Bartolucci F., Pennoni F. (2010). Modello di valutazione B, in Sperimentazione Valutatore Indipendente ai sensi dell‟art. 17 della l.r. n. 22/06 e dell'art. 27 della l.r. 19/07 relativamente al biennio di attivita' 2008/2010 Valutazione delle performance degli operatori beneficiari della l.r. 22/06 e della l.r. 19/07, (IReR 2008B076), pp. 172-195.

Pennoni F. (2007). European lifelong learning index (ELLI): Monitoring Lifelong Learing and its effects on economic prosperity and social well-being in the European and regional context. Feasibility Study, Final Report: December 2007.

Produzione Didattica (Università degli Studi di Milano-Bicocca):

• Pennoni, F. (2020). Dispensa relativa al modulo di Analisi Statistica Multivariata – Modelli Statistici- parte di teoria e applicazioni con R. Dipartimento di Statistica e Metodi Quantitativi, Università degli Studi di Milano-Bicocca.

• Pennoni, F. (2020). Dispense del modulo di Modelli Statistici II parte di teoria: Introduzione, Parte I e Parte II e Dispense R. Dipartimento di Statistica e Metodi Quantitativi, Università degli Studi di Milano-Bicocca.

• Pennoni, F. (2020). Dispense del modulo di Inferenza Bayesiana. Dipartimento di Statistica e Metodi Quantitativi, Università degli Studi di Milano-Bicocca.

• Pennoni, F. (2020). Dispense del modulo di Inferenza Bayesiana applicazioni con R e SAS.

• Pennoni, F. (2017). Dispense Modelli Statistici II Parte II. Dipartimento di Statistica e Metodi Quantitativi, Università degli Studi di Milano-Bicocca.

• Pennoni, F. (2016). Slides e relativi file Markdown con esempi ed esercizi dell’insegnamento di Inferenza Bayesiana e Modelli statistici II, Dipartimento di Statistica e Metodi Quantitativi, Università degli Studi di Milano-Bicocca

• Pennoni, F. (2016). Slides dell’Insegnamento di Introduction to Bootstrap e Introduction to the EM Algorithm per il Corso di Dottorato in Statistica e Matematica per la Finanza. Dipartimento di Statistica e Metodi Quantitativi, Università degli Studi di Milano-Bicocca.

• Pennoni, F. (2015). Slides dell’Insegnamento di Modelli Statistici II, Parte I e Parte II. Dipartimento di Statistica e Metodi Quantitativi, Università degli Studi di Milano-Bicocca.

• Pennoni, F. (2015). Slides dell’insegnamento di Controllo Statistico della Qualità, Parte I. Dipartimento di Statistica e Metodi Quantitativi, Università degli Studi di Milano-Bicocca.

• Pennoni, F. (2015). Slides dell’insegnamento di Probabilità ed Inferenza Statistica Parte I e Parte II e Parte III. Dipartimento di Statistica e Metodi Quantitativi, Università degli Studi di Milano-Bicocca.

• Pennoni, F. (2008). Dispense del corso di Metodi di Simulazione: Metodi di simulazione: esercitazioni in R, pp. 53. Dipartimento di Scienze Statistiche, Università degli Studi di Milano-Bicocca.

• Mecatti F., Pelagatti M., Pennoni F. (2008). Metodi di Simulazione. Dipartimento di Scienze Statistiche, Università degli studi di Milano-Bicocca, pp. 238.

Tesi di dottorato (Ph.D. Thesis):

Pennoni F. (2004). Issues on the Estimation of Latent Variable and Latent Class Models with Social Science Applications. Università degli Studi di Firenze. (The pdf file may be found here http://boa.unimib.it/handle/10281/46004)

A short introduction to the Ph.D. Thesis in Italian may be found here

Tesi di Laurea (Master Thesis):

Pennoni F. (2000). Metodi statistici multivariati applicati all’analisi del comportamento dei titolari di carta di credito di tipo revolving. Tesi di Laurea. Università degli Studi di Perugia. (http://hdl.handle.net/10281/50024)

Seminari (Talks)

A latent Markov model for detecting pattern of criminal activity

Reti Bayesiane ed Inferenza Causale metodologia ed Applicazioni,

Riunione Intermedia Cofin, 19-21 Dicembre 2004 Slides [PDF]

A hidden Markov model for the analysis for criminal behaviour classification

RSS2004: Connecting practise with research, Univerisity of Manchester, September 2004

Slides [PDF]

On the estimation of Linear Models Generated over a Directed Acyclic Graph with one Hidden Variable (Invited)

RC33: Sixth International Conference on Social Science Methodology, August 2004

Slides [PDF]

A latent Markov model to classifying criminal activity [poster presentation]

19th International Workshop on Statistical Modeling, University of Florence, July 2004

Poster [PDF]

On the Estimation of Path Analysis Models with one Hidden Variable

XLII Scientific Meeting Italian Statistical Society, Univerisity of Bari, June 2004

Modelling Latent Structures through conditional independence models

Methodology and Statistics, Ljubljna, September 2003

Slides [PDF]

Classifying criminal activity: a latent class approach to longitudinal event data

54th ISI Session, International Statistical Institute, Berlin, August 2003

Slides [PDF]

Research hypotheses on the latent structure of data in the social sciences

through conditional independence models

RSC2003, Annual Research Students' Conference in Probability and Statistics,

University of Surrey, April 2003.