FIRB Research project

“Mixture and latent variable model for causal inference and analysis of socio-economic data”



Journal Articles



1.    Bacci S., Bartolucci F. (in press), A Multidimensional Latent Class IRT Model for Non-Ignorable Missing Responses, Structural Equation Modeling: A Multidisciplinary Journal.

2.    Bartolucci F., Bellio R., Sartori N., Salvan A. (in press), Modified profile likelihood for fixed effects panel data models, Econometrics Reviews.

3.    Bartolucci, F., Farcomeni, A., Pennoni, F. (in press). Latent Markov Models: a review of a general framework for the analysis of longitudinal data with covariates, TEST.

4.    Bernini C., Matteucci M., Mignani S. (in press), Investigating heterogeneity in residents’ attitudes toward tourism with an IRT multidimensional approach, Quality & Quantity.

5.    Bia M., Flores A. C., Flores-Lagunes A., Mattei A. (in press), A Stata Package for the Application of Semiparametric Estimators of Dose-Response Functions, The STATA Journal.

6.    Bianconcini S. (in press). Asymptotic properties of adaptive maximum likelihood estimators in latent variable models, Bernoulli.

7.    Bianconcini S. (in press), Comments on: Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates, TEST.

8.    Ferraro M.B., Savarese M., Di Fruscio G., Nigro V., Guarracino M.R. (in press), Prediction of rare single-nucleotide causative mutations for muscular diseases in pooled NGS experiments, Journal of Computational Biology.

9.    Giordani P. (in press), Linear regression analysis for interval-valued data based on the Lasso technique, Advances in Data Analysis and Classification.

10.  Greselin F., Ingrassia S. (in press), Maximum likelihood estimation in constrained parameter spaces for mixtures of factor analyzers, Statistics and Computing.

11.  Ingrassia S., Minotti S.C., Punzo A., Vittadini G. (in press), The Generalized Linear Mixed Cluster-Weighted Model, Journal of Classification.

12.  Lovaglio P.G., Vacca G.,  Verzillo S. (in press). Human Capital Estimation in Higher Education, Advances in Data Analysis and Classification.

13.  Lovaglio P.G., Vittadini G. (in press), Structural Equation Models in a Redundancy Analysis Framework with Covariates, Multivariate behavioral statistics.

14.  Martini G.M.,  Berta P., Mullahy J., Vittadini G. (in press), The Effectiveness-Efficiency Trade-Off in Health Care: The Case of Hospitals in Lombardy, Italy,  Regional Science and Urban Economics.

15.  Mattei A., Mealli F., Pacini B. (in press), Identification of Causal Effects in the Presence of Nonignorable Missing Outcome Values, Biometrics.

16.  Matteucci M.,  Mignani S. (in press), Exploring regional differences in the reading competencies of Italian students, Evaluation Review.

17.  Modugno L., Giannerini S. (in press), The wild bootstrap for multilevel models, Communications in Statistics - Theory and Methods.

18.  Piccolo, D. (in press), Inferential issues on CUBE models with covariates, Communications in Statistics. Theory and Methods.

19.  Romeo I. and Fiore B. (in press). La Lombardia nell’indagine PISA 2012, Éupolis Lombardia. Rapporto Annuale 2013, Osservatorio del mercato del lavoro e della formazione.

20.  Sambucini V. (in press). Comparison of single-arm versus randomized phase II trials: a Bayesian approach, Journal of Biopharmaceutical Statistics.



21.  Bacci S., Bartolucci F. (2014), Mixtures of equispaced normal distributions and their use for testing symmetry with univariate data, Computational Statistics and Data Analysis, 71, 262-272.

22.  Bacci S., Bartolucci F., Gnaldi M. (2014), A class of Multidimensional Latent Class IRT models for ordinal polytomous item responses, Communication in Statistics - Theory and Methods, 43, 787-800.

23.  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.

24.  Bagnato L., Greselin F. and Punzo A. (2014), On the Spectral Decomposition in Normal Discriminant Analysis, Communications in Statistics - Simulation and Computation, 43, 1471–1489.

25.  Bartolucci F., Bacci S., Gnaldi M. (2014), MultiLCIRT: An R package for multidimensional latent class item response models, Computational Statistics and Data Analysis, 71, 971-985.

26.  Bartolucci F., Bacci S., Pennoni F. (2014), Longitudinal analysis of the self-reported health status by mixture latent autoregressive models, Journal of the Royal Statistical Society - series C, 63, 267-288.

27.  Bartolucci F., Farcomeni A. (2014), Information matrix for hidden Markov models with covariates, Statistics and Computing.

28.  Bartolucci F., Pandolfi S. (2014), A new constant memory recursion for hidden Markov models, Journal of Computational Biology, 21, 99-117.

29.  Bartolucci F., Pandolfi S. (2014), Comment on “On the memory complexity of the forward backward”, Pattern Recognition Letters, 38, 15-19.

30.  Bernini C.,  Cagnone S. (2014), Analysing tourist satisfaction at a mature and multi-product destination, Current Issues in Tourism, 17, 1-20.

31.  Bertoli-Barsotti L., Bacci S. (2014), Identifying Guttman structures in incomplete Rasch datasets, Communications in Statistics. Theory and Methods, 43(3), 470-497.

32.  Bianconcini S., Cagnone S. (2014), The role of posterior densities in latent variable models for ordinal data, Communications in Statistics - Theory and Methods, 43 (4), 681-692.

33.  Cagnone S., Viroli C. (2014), A Factor Mixture Model for Analyzing Heterogeneity and Cognitive Structure of Dementia, AStA Advances in Statistical Analysis, 98, 1-20.

34.  Colombi R.,  Kumbhakar S.,  Martini G.M., Vittadini G. (2014), Closed-Skew Normality in Stochastic Frontiers with Individual Effects and Long/Short Run Efficienty, Journal of Productivity Analysis, 1-14, doi: 10.1007/s11123-014-0386-y.

35.  Gambacorta R., Iannario M., Valliant R. (2014), Design-based inference in a mixture model for ordinal variables for a stage stratifies design, Australian & New Zealand Journal of Statistics, doi: 10.1111/j.1467-842X.XXX.

36.  Giordani P., Kiers H.A.L., Del Ferraro M.A. (2014), Three-way component analysis using the R package ThreeWay, Journal of Statistical Software, 57 (7), 1-23, URL

37.  Grilli L., Iannario M., Piccolo D., Rampichini C. (2014), Latent Class CUB Models, Advances in Data Analysis and Classification, 8(1), 105-119.

38.  Grilli L., Varriale R. (2014), Specifying Measurement Error Correlations in Latent Growth Curve Models with Multiple Indicators, Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, doi: 10.1027/1614-2241/a000082.

39.  Iannario M. (2014), Modelling Uncertainty and Overdispersion in Ordinal Data, Communications in Statistics. Theory and Methods, 43, 4, 771-786.

40.  Iannario M., Piccolo D. (2014), A theorem on CUB models for rank data, Statistics and Probability Letters, 91, 27-31.

41.  Ingrassia S., Minotti S. C.,  and Punzo A. (2014), Model-based clustering via linear cluster-weighted

Models, Computational Statistics & Data Analysis, 71, 159–182.

42.  Marelli, E., Sciulli, D., Signorelli, M. (2014), Skill Mismatch of Graduates in a Local Labour Market, Economy of Region, 2.

43.  Mattei A., Mealli F., Pacini B. (2014), Identification of Local Causal Effects with Missing Outcome Values and an Instrument for Nonresponse, Communications in Statistics - Theory and Methods, 43(4), 815-825.

44.  Matteucci M. (2014), An investigation of parameter recovery in MCMC estimation for the additive IRT model, Communication in Statistics - Theory and Methods, 43(4), 751-770.

45.  Mealli F., Pacini B. (2014), Using secondary outcomes to sharpen inference in randomized experiments with noncompliance, Journal of the American Statistical Association, 108, 1120-1131

46.  Pandolfi S., Bartolucci F., Friel N. (2014), A generalized Multiple-try Metropolis version of the Reversible Jump algorithm, Computational Statistics and Data Analysis, 72, 298-314.

47.  Punzo A. (2014), Flexible Mixture Modeling with the Polynomial Gaussian Cluster-Weighted Model, Statistical Modelling, 14, 1–35.

48.  Rocchetti I., Alfò M., Boehning D. (2014), A regression estimator for mixed binomial capture-recapture data, Journal of Statistical Planning and Inference, 145, 165-178.

49.  Vicari D., Alfò M. (2014), Model based clustering of customer choice data, Computational Statistics and Data Analysis, 71, 3-13.

50.  Viviani S., Alfó M., Rizopoulos D. (2014), Generalized linear mixed joint model for longitudinal and survival outcomes, Statistics and Computing, 24, 417-427.



51.  Alfò, M., Rocchetti, I. (2013), A flexible approach to finite mixture regression models for multivariate mixed responses, Statistics and Probability Letters, 84, 1754-1758

52.  Bagnato L., Punzo A. (2013), Finite mixtures of unimodal beta and gamma densities and the k-bumps algorithm, Computational Statistics, 28, 1571–1597.

53.  Bartolucci F., Farcomeni A. (2013), Causal inference in paired two-arm experimental studies under non-compliance with application to prognosis of myocardial infarction, Statistics in Medicine, 25, 4348-4366.

54.  Bee Dagum. E., Bianconcini S. (2013), A unified probabilistic view of nonparametric predictors via reproducing kernel Hilbert spaces, Econometric Reviews, 32(7), 848- 867.

55.  Berta P.,  Seghieri C., Vittadini G.  (2013), Comparing health outcomes among hospitals: the experience of the Lombardy Region, Health Care Management Science, 16, 245-257. DOI: 10.1007%2Fs10729-013-9227-1.

56.  Bertaccini B., Grilli L., Rampichini C. (2013), An IRT-MIMIC model for the analysis of university student careers, QdS - Journal of Methodological and Applied Statistics, 15, 95-110.

57.  Bini M., Grilli L., Rampichini C. (2011 – published in 2013), Contextual factors of the external effectiveness of the university education: a multilevel approach, Statistica Applicata - Italian Journal of Applied Statistics, 23 (1), 51-65.

58.  Cagnone S., Monari P. (2013), Latent variable models for ordinal data by using the adaptive quadrature approximation, Computational Statistics, 28, 597-619.

59.  Choudhry M., Marelli E., Signorelli M. (2013), Youth and Total Unemployment Rate: The Impact of Policies and Institutions, Rivista Internazionale di Scienze Sociali, 1.

60.  Colasante E., Gori M., Bastiani L., Siciliano V., Giordani P., Grassi M., Molinaro S. (2013), An assessment of the psychometric properties of Italian version of CPGI, Journal of Gambling Studies, 29, 765-774.

61.  Demidova O., Marelli E., Signorelli M. (2013) Spatial Effects on Youth Unemployment Rate: The Case of Eastern and Western Russian Regions, Eastern European Economics, 5.

62.  Ferraro M.B., Giordani P. (2013), On possibilistic clustering with repulsion constraints for imprecise data, Information Sciences, 245, 63-75.

63.  Giordani P., Rocci R. (2013), Candecomp/Parafac with ridge regularization, Chemometrics and Intelligent Laboratory Systems, 129, 3-9.

64.  Giordani P., Rocci R. (2013), Constrained Candecomp/Parafac via the Lasso, Psychometrika, 78, 669-684.

65.  Gnaldi M., Matteucci M., Mignani S., Falocci N. (2013), Methods of Item Analysis in Standardized Student Assessment: an Application to an Italian Case Study, The International Journal of Educational and Psychological Assessment, 12(2), pp. 78-92.

66.  Greselin F., Punzo A. (2013), Closed Likelihood Ratio Testing Procedures to Assess Similarity of Covariance Matrices, The American Statistician, 67, 117–128.

67.  Lovaglio P.G., Parabiaghi A. (2013), Assessment of meaningful change in routine outcome measurement (ROM) with a combination of a longitudinal and a ‘classify and count’ approach, Quality &  Quantity, 1-14, DOI 10.1007/s11135-013-9902-9.

68.  Lovaglio P.G., Vittadini G. (2013),  Multilevel Dimensionality-Reduction Methods, Statistical Methods & Applications,  22,  183-207 - DOI: 10.1007/s10260-012-0215-2.

69.  Mattei A., Li F., Mealli F. (2013), Exploiting Multiple Outcomes in Bayesian Principal Stratification Analysis with Application to the Evaluation of a Job Training Program, The Annals of Applied Statistics, 7(4), 2336-2360

70.  Matteucci M., Veldkamp B. P. (2013), On the use of MCMC computerized adaptive testing with empirical prior information to improve efficiency, Statistical Methods & Applications, 22 (2), 243-267.

71.  Mignani S. (2013), L’apprendimento della Statistica nella scuola: alcune considerazioni sui risultati dei test Invalsi, Statistica & Società, 2, 3, 45-48.

72.  Pennoni F., Vittadini G. (2013), Two competing models for ordinal longitudinal data with time-varying latent effects: an application to evaluate hospital efficiency, Journal of Methodological and Applied Statistics, 15, 53-68.

73.  Piccolo D., Capecchi S., Iannario M., Corduas M. (2013), Modelling consumer preferences for extra virgin olive oil: the Italian case, International Agricoltural Policy, 1, 25-37.

74.  Punzo A., Ingrassia S. (2013), On the use of the generalized linear exponential cluster-weighted model to asses local linear independence in bivariate data, QdS - Quaderni di Statistica, 15, 33–36.

75.  Riggi S., Ingrassia S. (2013), A model-based clustering approach for mass composition analysis of high energy cosmic rays, Astroparticle Physics, 48, 86–96.

76.  Sambucini V. (2013), On the Nature of the Stationary Point of a Quadratic Response Surface: A Bayesian Simulation-Based Approach, The American Statistician, 67(1), 33-41.

77.  Subedi S., Punzo A., Ingrassia S., McNicholas P. D. (2013), Clustering and Classification via Cluster-Weighted Factor Analyzers, Advances in Data Analysis and Classification, 7, 5–40.

78.  Veldkamp B. P., Matteucci M. (2013), Bayesian Computerized Adaptive Testing, Ensaio: Avaliação e Políticas Públicas em Educação, 21(78), 57-82.

79.  Veldkamp B.P., Matteucci M., de Jong M. (2013), Uncertainties in the item parameter estimates and robust automated test assembly, Applied Psychological Measurement, 37(2), 123-139.






1.    Matteucci M., Mignani S. (in press), Multidimensional IRT models to analyze learning outcomes of Italian students at the end of lower secondary school, in Millsap R.E., Bolt D.M., van der Ark L.A., Wang W.-C. (Eds.) New Developments in Quantitative Psychology, Presentations from the 78th Annual Psychometric Society Meeting, Springer Proceedings in Mathematics & Statistics, 89.

2.    Matteucci M., Pillati M. (in press), The Unity of Italy from the point of view of student performances: evidences from PISA 2009, in Selected Issues in Statistical Methods and Applications in an Historical Perspective, Studies in Theoretical and Applied Statistics, Springer-Verlag Berlin Heidelberg.

3.    Mealli F., Pacini B., Stanghellini E. (in press), Identification of principal causal effects using secondary outcomes, in Carpita M., Brentari E., Qannari E. M. (Eds.), Advances in Latent  Variables: Methods, Models and Applications. Springer, Series Studies in Theoretical and Applied Statistics – Selected Papers from the Statistical Societies.



4.    Bruno G.S.F., Tanveer Choudhry M., Marelli, E., Signorelli M. (2014), Youth Unemployment: Key Determinants and the Impact of Crises, in Malo M. A. , Sciulli D. (Eds.), Disadvantaged Workers: Empirical Evidence and Labour Policies, Springer.

5.    Capecchi S., Piccolo D. (2014), Modelling the Latent components of Personal Happiness, in Perna M., Sibillo (Eds.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, Springer-Verlag, 49-52.

6.    Cellamare M., Sambucini V., Siena F. (2014), Randomized phase II trials: a Bayesian two-stage design, in The Contribution of Young Researchers to Bayesian Statistics - Proceedings of BAYSM 2013, Springer International Publishing, 63, 139-142.

7.    Ferraro M.B., Guarracino M.R. (2014), From separating to proximal plane classifiers: a review, in Pardalos, P.M.,Du, D-.Z. (Eds.) Springer Optimization and Its Applications, Clusters, orders, trees: methods and applications, Springer-Verlag Berlin Heidelberg.

8.    Giordani P. (2014), Principal Component Analysis, in Alhajj R., Rokne J. (Eds.), Encyclopedia of Social Network Analysis and Mining, Springer, Berlin.

9.    Romeo I., Fiore B. (2014), A Value Added Approach in Upper Secondary Schools of Lombardy by OCSE-PISA 2009 data, in Vicari D., Okada  A. , Ragozini  G., Weihs C. (Eds.),  Analysis and Modeling of Complex Data in Behavioral and Social Sciences. Springer.



10.  Blanco-Fernández A., Casals R.M., Colubi A., Coppi R., Corral N., de la Rosa de Sáa S., D’Urso P., Ferraro M.B., García-Bárzana M., Gil M.A., Giordani P., González-Rodríguez G., López M.T., Lubiano M.A., Montenegro M., Nakama T., Ramos-Guajardo A.B., Sinova B., Trutschnig W. (2013), Arithmetic and distance-based approach to the statistical analysis of imprecisely valued data, in Borgelt C., Gil M.A., Sousa J.M.C., Verleysen M. (Eds.) Towards Advanced Data Analysis by Combining Soft Computing and Statistics. Studies in Fuzziness and Soft Computing, Springer Verlag, Berlin Heidelberg, 285, 1-18.

11.  Coppi R., Ferraro M.B, Giordani P. (2013), A class of linear regression models for imprecise random elements, in Torelli N., Pesarin F., Bar-Hen A. (Eds.), Advances in Theoretical and Applied Statistics, Springer-Verlag Berlin Heidelberg, 211-220.

12.  Ferraro M.B., Coppi R., Gonzalez-Rodriguez G. (2013), Bootstrap Confidence Intervals for the Parameters of a Linear Regression Model with Fuzzy Random Variables, in Borgelt C., Gil M.A., Sousa J.M.C., Verleysen M. (Eds.) Towards Advanced Data Analysis by Combining Soft Computing and Statistics. Studies in Fuzziness and Soft Computing, 285, 33-42.

13.  Ferraro M.B., Giordani P. (2013), A proposal of robust regression for random fuzzy sets, in Kruse R., Berthold M.R., Moewes C., Gil M.A., Grzegorzewski P., Hryniewicz O. (Eds.), Synergies of Soft Computing and Statistics for Intelligent Data Analysis, Springer Verlag, Berlin, 115-123.

14.  Ferraro, M.B., Irpino, A., Verde, R., Guarracino, M.R. (2013), A novel feature selection method for classification using a fuzzy criterion, in Nicosia G., Pardalos P. (Eds.), LION 7- Lecture Notes in Computer Sciences, 7997, 1-13, Springer-Verlag Berlin Heidelberg.

15.  Grilli L., Rampichini C. (2013), Specification of random effects in multilevel models: an overview with focus on school effectiveness, in Brentari E., Carpita M. (Eds),  Advances in latent variablesVita e Pensiero, Brescia.

16.  Grilli L., Rampichini C., Varriale R. (2013) A concomitant variable mixture model for predicting freshmen gained credits, in Brentari E., Carpita M. (Eds),  Advances in latent variablesVita e Pensiero, Brescia.

17.  Lovaglio P.G.,  Vittadini  G. (2013), Component analysis for structural equation models with concomitant indicators, in Giudici P., Ingrassia S., Vichi M., (Eds.), Statistical Models for Data Analysis, Springer, Heidelberg.

18.  Marelli E., Signorelli M. (2013), The Unemployment Impact of Financial Crises, in Fadda S., Tridico P. (eds.), Financial Crises, Labour Markets and Institutions, Routledge, London.

19.  Mattei A., Mealli F., Pacini B. (2013), Exploiting Multivariate Outcomes in Bayesian Inference for Causal Effects with Noncompliance, in Torelli N., Pesarin F., Bar-Hen A. (Eds.), Studies in Theoretical and Applied Statistics. Part III: Statistical Modelling and Data Analysis, Springer, 231-241.

20.  Matteucci M., Veldkamp B. P. (2013), Bayesian estimation of item response theory models with power priors, in Brentari E., Carpita M. (Eds.), Advances in Latent Variables, Vita e Pensiero, Milan, Italy, 1-8.

21.  Pennoni F. (2013). Studying employment pathways of graduates by a latent Markov model, in Brentari E., Carpita M. (Eds),  Advances in latent variablesVita e Pensiero, Brescia, 1-6.

22.  Romeo I., Raffinetti E. (2013). Un’analisi statistica delle determinanti dei risultati degli studenti e delle scuole lombarde, in  Agasisti T., Catalano G., Vittadini G.  (Eds.).  Rapporto sulla scuola in Lombardia. Strumenti di analisi e di policy.  Guerini Editore. 

23.  Romeo I. ,Sibiano P.  (2013), Il sistema scolastico lombardo: un’analisi intra-regionale, in  Agasisti T., Catalano G., Vittadini G.  (Eds.), Rapporto sulla scuola in Lombardia. Strumenti di analisi e di policy. Guerini Editore.

24.  Vittadini G., Galan A. (2013), Introduzione, in Dall’uniformità alla differenziazione, Le politiche pubbliche sull’università in Lombardia, Il Mulino, Bologna, 11-19.



1.    Pennoni F. (2014). Issues on the estimation of latent variable and latent class models with social science applications. Scolar Press.

2.    Bartolucci F., Farcomeni A., Pennoni F. (2013). Latent Markov models for longitudinal data, Chapman and Hall/CRC, Boca Raton.