Publications

List of publications (see also my profile in Google Scholar, WoS, RePec, ORCID, ResearchGate, Mendeley, Amazon, Scopus)

Journals where I published

BOOKS

1. Bartolucci, F., Bacci, S., and Gnaldi, M. (2015), Statistical Analysis of Questionnaires: A Unified Approach Based on Stata and R, Chapman and Hall/CRC press.

2. Bartolucci, F., Farcomeni, A. and Pennoni, F. (2013), Latent Markov Models for Longitudinal Data, Chapman and Hall/CRC press, Boca Raton, FL.

ARTICLES IN SCIENTIFIC JOURNALS (Journals where I published)

1. Pennoni, F., Pandolfi, S., and Bartolucci, F. (2025), LMest: An R Package for Estimating Generalized Latent Markov Models, The R Journal, 16, pp. 74-101.

2. Ranalli, M. G., Pennoni, F., Bartolucci, F., and 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, pp. 515–543 .

3. Bartolucci, F, Pandolfi, S., and Pennoni, F. (2025), On a class of finite mixture models that includes hidden Markov models, Journal of Multivariate Analysis, 208, 105423.

4. Bartolucci, F. and Pennoni, M. (2024), Some useful illustrations of Fisher information, Electronic Journal of Applied Statistical Analysis, 17, pp. 572-585.

5. Bartolucci, F., Mira, A., and Peluso, S. (2024), Marginal models with individual-specific effects for the analysis of longitudinal bipartite networks, Advances in Data Analysis and Classification, in press.

6. Gourgoura, K. , Rivadeneyra, P. , Stanghellini, E.., Caroni, C. , Bartolucci, F., Curcio, R., Bartoli, S., Ferranti, R., Folletti, I., Cavallo, M., Sanesi,  L., Dominioni, I., Santoni, E., Morgana, G. , Pasticci, M. B., Pucci, G., and Vaudo, G. (2024), Modelling the long-term health impact of COVID-19 using Graphical Chain Models brief heading: long COVID prediction by graphical chain models, BMC Infectious Diseases, 24:885.

7. Bartolucci, F., Pigini, C., and Valentini, F. (2024), MCMC conditional maximum likelihood for the two-way fixed-effects logit, Econometric Reviews, 43, pp. 379-404.

8. Bartolucci, F. and Forcina A. (2024), Estimating the size of a closed population by modeling latent and observed heterogeneity, Biometrics, 80, ujae017.

9. Cortese, F. P., Pennoni, F. and Bartolucci, F. (2024), Maximum Likelihood Estimation of Multivariate Regime Switching Student-t Copula Models, International Statistical Review, 92, pp. 327-354.

10. Bartolucci, F., Favaro, D., Pennoni, F., and Sciulli, D. (2024), An Analysis of the Effect of Streaming on Civic Participation Through a Causal Hidden Markov Model, Social Indicators Research, 172, pp. 163-190.

11. Pennoni, F., Pandolfi, S., and Bartolucci, F. (2024), Variable Selection for Hidden Markov Models with Continuous Variables and Missing Data, Journal of Classification, 41, pp. 568-589.

12. Brusa, L., Pennoni, F., and Bartolucci, F. (2024), Maximum likelihood estimation for discrete latent variable models via evolutionary algorithms, Statistics and Computing, 34:62.

13. Tancini, D., Bartolucci, F., and Pandolfi, S. (2024), A comparison between marginal likelihood and data augmented MCMC algorithms for Gaussian hidden Markov models, Journal of Computational and Simulation, 94, pp. 1571-1594.

14. Pennoni, F., Paas, L., and Bartolucci, F. (2023), A causal hidden Markov model for assessing effects of multiple direct mail campaigns, TEST, 32, pp. 1336-1364.

15. Sherratt, K. et al. (2023), Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations, eLife, Epidemiology and Global Health.

16. Pandolfi, S., Bartolucci, F., and Pennoni, F. (2023), A hidden Markov model for continuous longitudinal data with missing responses and dropout, Biometrical Journal, 65:5, 2200016.

17. Bartolucci, F., Li Donni, P., and Mira, A. (2023), Temporal analysis of hospital network data by hierarchical Bayesian p2 models with covariates,  Journal of the Royal Statistical Society, Series A, 186, pp. 422-440.

18. Bartolucci, F., Pennoni, F., and 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, pp. 387-419.

19. Bartolucci, F., Pigini, C., and Valentini, F. (2023), Testing for state dependence in the fixed-effects ordered logit model, Economics Letters, 222:110964.

20. Bartolucci, F., Valentini, F., and Pigini, C. (2023), Recursive computation of the conditional probability function of the quadratic exponential model for binary panel data, Computational Economics, 61, pp. 529-557.

21. Bartolucci, F., Pigini, C., and Valentini, F. (2023), Conditional inference and bias reduction for partial effects estimation of fixed-effects logit models, Empirical Economics, 64, pp. 2257–2290.

22. Brusa, L., Bartolucci, F., and Pennoni, F. (2022), Tempered expectation-maximization algorithm for the estimation of discrete latent variable models, Computational Statistics, 38, pp. 1391-1424.

23. Tullio, F. and Bartolucci, F. (2022), Causal inference for time-varying treatments in latent Markov models: An application to the effects of remittances on poverty dynamics, Annals of Applied Statistics, 16, pp. 1962-1985. 

24. Federici, S., Lepri, A., Bacci, S., Bartolucci, F. (2022), Male recognition bias in sex assignment based on visual stimuli,  Scientific Reports, 12:8156.

25. Bartolucci, F., Pandolfi, S., and Pennoni, F. (2022), Discrete latent variable models, Annual Review of Statistics and its Application, 9, pp. 425-452.

26. Bartolucci, F. and Farcomeni, A. (2022), A hidden Markov space–time model for mapping the dynamics of global access to food, Journal of the Royal Statistical Society - Series A, 185, pp. 246-266.

27. Agresti, A., Bartolucci, F., and Mira, A. (2022), Reflections on Murray Aitkin's contributions to nonparametric mixture models and Bayes factors, Statistical Modelling, 22, pp. 33-45.

28. Pennoni, F., Bartolucci, F., Forte, G., and Ametrano, F. (2022), Exploring the dependencies among main cryptocurrency log-returns: A hidden Markov model, Economic Notes, 51:e12193.

29. Bartolucci, F. and Farcomeni, A. (2022), A spatio-temporal model based on discrete latent variables for the analysis of COVID-19 incidence, Spatial Statistics, 49:100504.

30. Genge, E. and Bartolucci, F. (2022), Are attitudes toward immigration changing in Europe? An analysis based on latent class IRT models, Advances in Data Analysis and Classification, 16, pp. 235-271.

31. Pigini, C. and Bartolucci, F. (2022), Conditional inference for binary panel data models with predetermined covariates, Econometrics and Statistics, 23, 83-104.

32. Bartolucci, F., Pennoni, F., and Mira, A. (2021), A multivariate statistical approach to predict COVID-19 count data with epidemiological interpretation and uncertainty quantification, Statistics in Medicine, 40, pp. 5351-5372.

33. Ghiringhelli, C., Bartolucci, F., Mira, A., and Arbia, G. (2021), Modelling Nonstationary Spatial Lag Models with Hidden Markov Random Fields, Spatial Statistics, 44, 100522.

34. Aristei, D., Bacci, S., Bartolucci, F., and Pandolfi, S. (2021), A bivariate finite mixture growth model with selection, Advances in Data Analysis and Classification, 15, pp. 759–793.

35. Bartolucci, F. and Pandolfi, S. (2020), An exact algorithm for time-dependent variational inference for the dynamic stochastic block model, Pattern Recognition Letters, 138, pp. 362-369.

36. Favaro, D., Sciulli, D., and Bartolucci, F. (2020), Primary-school class composition and the development of social capital, Socio-Economic Planning Sciences, 72, 100874.

37. Bianchi, F., Bartolucci, F., Peluso, S., and Mira, A. (2020),  Longitudinal Networks of Dyadic Relations Using Latent Trajectories: Evidence from the European Interbank Market, Journal of the Royal Statistical Society - Series C, 69, pp. 711-739.

38. Bacci, S., Bartolucci, F., Bettin, S., and Pigini, C. (2019), A latent class growth model for migrants' remittances: An application to the German Socio-Economic Panel, 182, Journal of the Royal Statisticsl Society – Series A, 182, pp. 1607-1632.

39. Bartolucci, F., Farcomeni, A. (2019), A shared-parameter continuous-time hidden Markov and survival model for longitudinal data with informative drop-out, Statistics in Medicine, 38, pp. 1056-1073.

40. Rajeziesfahani, S., Federici, S. , Bacci, S., Meloni, F., Bartolucci, F., Zahiroddin, A., Shams, J., and Noorbakhshe, S. (2019), Validity of the 36-item Persian (Farsi) version of the world health organization disability assessment schedule (WHODAS) 2.0, International Journal of Mental Health,  48, p. 14-39 .

41. Montanari, G. E., Doretti, M., Bartolucci, F. (2018), A multilevel latent Markov model for the evaluation of nursing homes' performance, Biometrical Journal, 60, pp. 962-978.

42. Cagini, L., Andolfi, M., Becattini, C., Ranalli, M. G., Bartolucci, F., Mancuso, A., Vannucci, J., Agnelli, G., and Puma, F. (2018), Bedside sonography assessment of extravascular lung water increase after major pulmonary resection in non-small cell lung cancer patients, Journal of Thoracic Disease, 10(7).

43. Bertarelli, G., Ranalli, M. G., Bartolucci, F., D’Alò, M., and Solari, F. (2018), Small area estimation of unemployment using Latent Markov Models, Survey Methodology, 44, 167-192.

44. Bartolucci, F., Bacci, S., & Mira, A. (2018), On the role of latent variable models in the era of big data, Statistics and Probability Letters, 136, pp. 165-169.

45. Bartolucci, F., Choudrhy, M. T., Marelli, E.,  and Signorelli, M. (2018), GDP Dynamics and Unemployment Changes in Developed and Developing Countries, Applied Economics, 50, pp. 3338-3356.

46. Bartolucci, F., Marino, F. & Pandolfi, S. (2018), Dealing with Reciprocity in Dynamic Stochastic Block Models, Computational Statistics and Data Analysis, 123, pp. 86-100.

47. 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.

48. 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 (previous technical report: http://www.econ.unipg.it/files/generale/wp_2015/WP_14_2015_Bacci_Bartolucci_Pandolfi.pdf).

49. Bartolucci, F., Nigro, V. & Pigini, C. (2018), Testing for State Dependence in Binary Panel Data with Individual Covariates by a Modified Quadratic Exponential Model, Econometric Reviews, 37, pp. 61-88.

50. Bartolucci, F., Burno, G. S. F., Demidova, O., and Signorelli, M. (2017), Job satisfaction and compensating wage differentials: Evidence from Russia, CESifo Economic Studies, 63, 333-351.

51. Bacci, S., Bartolucci, F., Grilli, L., and Rampichini, C. (2017), Evaluation of student performance through a multidimensional finite mixture IRT model, Multivariate Behavioral Research, 52, pp. 732-746 (previous technical report: https://arxiv.org/pdf/1609.06465.pdf).

52. Bartolucci, F., Farcomeni, A., and Scaccia, L. (2017), A nonparametric multidimensional latent class IRT model in a Bayesian framework, Psychometrika, 82, pp. 952–978.

53. 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(4). 

54. Bartolucci, F. and Pigini, C. (2017), cquad: An R and Stata Package for Conditional Maximum Likelihood Estimation of Dynamic Binary Panel Data Models, Journal of Statistical Software, 78, pp. 1-26 (previous technical report: https://mpra.ub.uni-muenchen.de/67030/).

55. Bartolucci, F., Chiaromonte, F., Kuruppumullage Don, P., & Lindsay, B. G. (2017), Composite likelihood inference in a discrete latent variable model for two-way "clustering-by-segmentation" problems, Journal of Computational and Graphical Statistics, 26, pp. 388-402 (previous technical report: http://arxiv.org/abs/1506.08278).

56. Cagnone, S. and Bartolucci, F. (2017), Adaptive quadrature for maximum likelihood estimation of a class of dynamic latent variable models, Computational Economics, 49, pp. 599-622 (previous technical report: http://mpra.ub.uni-muenchen.de/51037/).

57. Bartolucci, F.,Bacci, S., and Pigini, C. (2017), Misspecification test for random effects in generalized linear finite-mixture models for clustered binary and ordered data, Econometrics and Statistics, 3, pp. 112-131.

58. Bartolucci, F. & Lupparelli, M. (2016), Pairwise likelihood inference for nested hidden Markov chain models for multilevel longitudinal data, Journal of the American Statistical Association, 111, pp. 216-228.

59. Bacci, S. and Bartolucci, F. (2016), Two-Tier Latent Class IRT Models in R, The R Journal, 8, pp. 139-167.

60. Bartolucci, F., Bellio, R., Sartori, N. & Salvan, A. (2016), Modified profile likelihood for fixed-effects panel data models, Econometric Reviews, 35, pp. 1271-1289 (previous techinal report: http://ssrn.com/abstract=2000666).

61. Bartolucci, F., Montanari, G.E., & Pandolfi, S. (2016), Item selection by latent class-based methods: An application to nursing home evaluation, Advances in Data Analysis and Classification, 10, pp. 245-262.

62. 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, pp. 146-179 (prevoius technical report: https://mpra.ub.uni-muenchen.de/66492/).

63. Gnaldi, M., Bacci, S. & Bartolucci, F. (2016), A multilevel finite mixture item response model to cluster examinees and schools, Advances in Data Analysis and Classification, 10, pp. 53-70.

64. Bacci, S., Bartolucci, F., Minelli, L., Chiavarini, M. (2016), Preterm Birth: Analysis of Longitudinal Data on Siblings Based on Random-Effects Logit Models, Frontiers in Public Health: Population, Reproductive and Sexual Health, doi.org/10.3389/fpubh.2016.00278.

65. Bartolucci, F. & Farcomeni, A. (2015), A discrete time event-history approach to informative drop-out in mixed latent Markov models with covariates, Biometrics, 71, pp. 80-89 (previous tecnica report: http://arxiv.org/pdf/1306.1678.pdf).

66. Bartolucci, F., Belotti, F. & Peracchi, F. (2015), Testing for Time-Invariant Unobserved Heterogeneity in Generalized Linear Models for Panel Data, Journal of Econometrics, 184, pp. 111-123 (previous technical report: EIEF 12/13).

67. Bacci, S., Bartolucci, F. (2015), A multidimensional finite mixture structural equation model for nonignorable missing responses, Structural Equation Modeling A Multidisciplinary Journal, 22, pp. 352-365 (previous techincal report: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2213721).

68. Bartolucci, F. (2015), A comparison between g-index and h-index based on concentration, Journal of the Association for Information Science and Technology, 66, pp. 2708–2710.

69. Bartolucci, F. and Murphy, B. (2015), A finite mixture latent trajectory model for modeling ultrarunners' behavior in a 24-hour race, Journal of Quantitative Analysis of Sports, 11, pp. 193-203.

70. Bartolucci, F. & Farcomeni, A. (2015), Information matrix for hidden Markov models with covariates, Statistics and Computing, 25, pp. 515-526.

71. Bartolucci, F., Dardanoni, V. and Peracchi, F. (2015), Ranking scientific journals via latent class models for polytomous item response data, Journal of the Royal Statistical Society - Series A, 178, pp. 1025–1049 (previous technical report: EIEF 13/13).

72. Bartolucci, F., Montanari, G. E. and Pandolfi, S. (2015), Three-step estimation of latent Markov models with covariates, Computational Statistics and Data Analysis, 83, pp. 287-301 (previous technical report: http://arxiv.org/pdf/1402.1033v1.pdf).

73. Borsci, S., Federici, S., Bacci, S., Gnaldi, M., & Bartolucci, F. (2015), Assessing User Satisfaction in the Era of User Experience: Comparison of the SUS, UMUX and UMUX-LITE as a Function of Product Experience, International Journal of Human-Computer Interaction, 31, pp. 484-495.

74. 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, pp. 262-272 (previous technical report: http://arxiv.org/abs/1204.4544).

75. Bacci, S., Bartolucci, F. Chiavarini, M., Minelli, L. & Pieroni, L. (2014), Differences in Birthweight Outcomes: A Longitudinal Study Based on Siblings, International Journal of Environmental Research and Public Health, 11, pp. 6472-6484 (previous tecnical report: http://mpra.ub.uni-muenchen.de/55789/).

76. 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, pp. 787-800 (previous tecnical report: http://arxiv.org/abs/1201.4667).

77. Bartolucci, F. & Pandolfi, S (2014), A new constant memory recursion for hidden Markov models, Journal of Computational Biology, 21, pp. 99-117 (previous tecnical report: http://arxiv.org/abs/1201.0277).

78. Bartolucci, F., Bacci, S. & Gnaldi, M. (2014), MultiLCIRT: An R package for multidimensional latent class item response models, Computational Statistics and Data Analysis, 71, pp. 971-985 (previous technical report: http://arxiv.org/abs/1210.5267).

79. 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, pp. 267-288. (previous technical report: http://arxiv.org/abs/1108.1498).

80. Bartolucci, F., Farcomeni, A., and Pennoni, F. (2014), Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates (with discussion), Test, 23, pp. 433-486.

81. Minelli, L., Pigini, C., Chiavarini, M. & Bartolucci, F. (2014), Employment status and perceived health condition: longitudinal data from Italy, BMC Public Health, 14: 946 (previous technical report: MPRA 55788).

82. 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 (previous thecnical report: http://arxiv.org/abs/1006.0621).

83. 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, pp. 4348-4366 (previous technical report: http://arxiv.org/abs/1210.6678).

84. Bartolucci, F., Bacci, S., Pigini, C. (2013), Comparison between conditional and marginal maximum likelihood estimation for a class of ordinal item response models, QdS - Journal of Methodological and Applied Statistics, 15, pp. 1-17.

85. Bacci, S. & Bartolucci, F. (2012), Mixtures of equispaced Normal distributions and their use for testing symmetry in univariate data, Quaderni di Statistica, 14, pp. 13-16.

86. Bartolucci, F. & Nigro, V. (2012), Pseudo conditional maximum likelihood estimation of the dynamic logit model for binary panel data, Journal of Econometrics, 170, pp. 102-116. (previous technical report arXiv:math/0702774 and http://ssrn.com/abstract=1081146)

87. Bartolucci, F., Montanari, G.E. & S. Pandolfi (2012), Dimensionality of the latent structure and item selection via latent class multidimensional IRT models, Psychometrika, 77, pp. 782-802.

88. Bartolucci, F., Scaccia, L. & Farcomeni, A. (2012), Bayesian inference through encompassing priors and importance sampling for a class of marginal models for categorical data, Computational Statistics and Data Analysis, 56, pp. 4067-4080. (previous technical report: http://arxiv.org/abs/1202.4074)

89. Chiavarini, M., Bartolucci, F., Gili, A., Pieroni & L., Minelli, L. (2012), Effects of Individual and Social Factors on Preterm Birth and Low Birth Weight: an Italian case study, International Journal of Public Health, 57, pp. 261-268.

90. Bartolucci, F. & Grilli, L. (2011), Modelling partial compliance through copulas in a principal stratification framework, Journal of the American Statistical Association, 106, pp. 469-479.

91. 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, pp. 491-522. (previous Technical report: arxiv.org/abs/0909.4961v1).

92. Bartolucci, F. & Nigro, V. (2010), A Dynamic Model for Binary Panel Data with Unobserved Heterogeneity Admitting a root-n Consistent Conditional Estimator, Econometrica, 78, pp. 719-733.

93. Bartolucci, F. (2010), On the conditional logistic estimator in two-arm experimental studies with non-compliance and before-after binary outcomes, Statistics in Medicine, 29, pp. 1411-1429 (previous tech. Technical report: arXiv:0710.2608).

94. Pandolfi, S., Bartolucci, F. & Friel, N. (2010), A generalization of the Multiple-try Metropolis algorithm for Bayesian estimation and model selection, Journal of Machine Learning Research Workshop and Conference Proceedings, Volume 9: AISTATS 2010, pp. 581-588.

95. Bartolucci, F. & Farcomeni, A. (2010), A Note on the Mixture Transition Distribution and Hidden Markov Models, Journal of Time Series Analysis, 31, pp. 132-138.

96. Bartolucci, F. & Solis-Trapala, I. (2010), Multidimensional latent Markov models in a developmental study of inhibitory control and attentional flexibility in early childhood, Psychometrika, 75, pp. 725-743.

97. Bartolucci, F. & Farcomeni, A. (2009), A multivariate extension of the dynamic logit model for longitudinal data based on a latent Markov heterogeneity structure, Journal of the American Statistical Association, 104, pp. 816-831.

98. Bartolucci, F., Lupparelli, M. & Montanari, G. E. (2009), Latent Markov model for longitudinal binary data: an application to the performance evaluation of nursing homes, Annals of Applied Statistics, 3, pp. 611-636. (previous Technical report: arXiv:0908.2300)

99. Bartolucci, F. & Lupparelli, M. (2008), Focused information criterion for capture-recapture models for closed populations, Scandinavian Journal of Statistics, 35, pp. 629 – 649.

100. Bartolucci, F. & Pennoni, F. (2007), A class of latent Markov models for capture-recapture data allowing for time, heterogeneity and behavior effects, Biometrics, 63, pp. 568-578.

101. Bartolucci, F. & Pennoni, F. (2007), On the approximation of the quadratic exponential distribution in a latent variable context, Biometrika, 94, pp. 745-754.

102. Bartolucci, F. (2007), A class of multidimensional IRT models for testing unidimensionality and clustering items, Psychometrika, 72, 141-157.

103. Bartolucci, F. (2007), A penalized version of the empirical likelihood ratio for the population mean, Statistics and Probability Letters, 77, pp. 104-110.

104. Bartolucci, F. & Nigro, V. (2007), Maximum likelihood estimation of an extended latent Markov model for clustered binary panel data, Computational Statistics and data Analysis, 51, pp. 3470-3483.

105. Bartolucci, F., Colombi, R. & Forcina, A. (2007), An extended class of marginal link functions for modelling contingency tables by equality and inequality constraints, Statistica Sinica, 17, pp. 691-711.

106. Bartolucci, F., Pennoni, F. & Francis, B. (2007), A latent Markov model for detecting patterns of criminal activity, Journal of the Royal Statistical Society, series A, 170, pp. 115–132.

107. Bartolucci, F. (2006), Likelihood inference for a class of latent Markov models under linear hypotheses on the transition probabilities, Journal of the Royal Statistical Society, series B, 68, pp. 155-178.

108. Bartolucci, F. & Forcina, A. (2006), A class of latent marginal models for capture-recapture data with continuous covariates, Journal of the American Statistical Association, 101, pp. 786-794.

109. Bartolucci, F., Scaccia, L. & Mira, A. (2006), Efficient Bayes factor estimation from the Reversible Jump output, Biometrika, 93, pp. 41-52.

110. Bartolucci, F. & Montanari, G. E. (2006), A new class of unbiased estimators for the variance of the systematic sample mean, Journal of Statistical Planning and Inference, 136, pp. 1512-1525.

111. Bartolucci, F. (2005), Clustering univariate observations via mixtures of unimodal normal mixtures, Journal of Classification, 22, pp. 203-219.

112. Bartolucci, F. & Forcina, A. (2005), Likelihood inference on the underlying structure of IRT models, Psychometrika, 70, p. 31-43.

113. Bartolucci, F. & Scaccia, L. (2005), The use of mixtures for dealing with non-normal regression errors, Computational Statistics and Data Analysis, 48, pp. 821-834 (tables for the homeschedastic case).

114. Bartolucci, F. & Scaccia, L. (2004), Testing for positive association in contingency tables with fixed margins, Computational statistics and Data Analysis, 47, pp. 195-210.

115. Forcina, A. & Bartolucci, F. (2004), Modelling quality of life variables with non-parametric mixtures, Environmetrics, 15, pp. 519-528.

116. Bartolucci, F. & De Luca, G. (2003), Likelihood-based inference for asymmetric stochastic volatility models, Computational Statistics and Data Analysis, 42, pp. 445-449.

117. Bartolucci, F. & Besag, J. (2002), A recursive algorithm for Markov random fields, Biometrika, 89, pp. 724-730.

118. Bartolucci, F. & Forcina, A. (2002), Extended RC association models allowing for order restrictions and marginal modelling, Journal of the American Statistical Association, 97, pp. 1192-1199.

119. Bartolucci, F., Forcina, A. & Dardanoni, V. (2001), Positive Quadrant Dependence and Marginal Modelling in two-way tables with ordered margins, Journal of the American Statistical Association, 96, pp. 1497-1505.

120. Bartolucci, F. & Forcina A. (2001), Analysis of capture-recapture data with a Rasch-type model allowing for conditional dependence and multidimensionality, Biometrics, 57, pp. 714-719.

121. Bartolucci, F. & De Luca, G. (2001), Maximum likelihood estimation for a latent variable time series model, Applied Stochastic Models for Business and Industry, 17, pp. 5-17.

122. Bartolucci, F. (2001), Developments of the Markov chain approach within the distribution theory of runs, Computational Statistics and Data Analysis, 36, pp. 107-118.

123. Bartolucci, F. & Forcina A. (2000), A likelihood ratio test for MTP2 within binary variables, The Annals of Statistics, 28, pp. 1206-1218.

124. Bartolucci, F. & Montanari, G. E. (1998), On estimating the variance of the systematic sample mean, Journal of the Italian Statistical Society, 7, pp. 185-196.

CHAPTERS IN INTERNATIONAL BOOKS

1. Valentini, F., Pigini, C., and Bartolucci, F. (2023), Advances in Maximum Likelihood Estimation of Fixed-Effects Binary Panel Data Models, in M. Kateri, I. Moustaki (eds.), Trends and Challenges in Categorical Data Analysis, pp. 275-315.

2. Bacci, S., Bartolucci, F., and Pennoni, F. (2020). Multilevel Model-Based Clustering: A New Proposal of Maximum-A-Posteriori Assignment. in Advanced Studies in Classification and Data Science, pp. 3-17, Springer.

3. Bartolucci, F., Cardinali, A., and Pennoni, F. (2018), A generalized moving average convergence/divergence for testing semi-strong market efficiency, in M. Corazza, M. Durbán, A. Grané, C. Perna, M. Sibilillo (eds.), Mathematical and Statisical Methods for Actuarial Sciences and Finance, pp. 101-105, Springer.

4. Bartolucci F., Bashina A., G. Bruno, O. Demidova, and M. Signorelli (2017), Job Satisfaction Among Young Workers, in Young People and the Labour Market: A Comparative Perspective, F.E. Caroleo, O. Demidova, E. Marelli and M. Signorelli (Eds.), Routledge, forthcoming.

5. Bartolucci, F. and Forcina, A. (2017), Latent class - Rasch models and marginal extensions, in Capture-Recapture Methods for the Social andMedical Sciences, D. Bohning, J. Bunge, and P. van der Heijden (Eds.), Chapman & Hall/CRC, in press.

6. Bacci, S., Bartolucci, F., Pigini, C., and Signorelli, M. (2016), A finite mixture latent trajectory model for the trend of open-ended contracts, In Di Battista T., Moreno E. and Racugno W., Topics on Methodological and Applied Statistical Inference, pp. 9-20, Springer.

7. Bartolucci, F. (2014), Modeling Longitudinal Data by Latent Markov Models with Application to Educational and Psychological Measurement, in Analysis and Modeling of Complex Data in Behavioural and Social Sciences, D. Vicari, A. Okada, G. Ragozini, C. Weihs (Eds.), Springer, pp. 11-19. (previous tecnical report: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2185392).

8. Bacci, S. & Bartolucci, F. (2012), A multidimensional latent class Rasch model for the assessment of the Health-related Quality of Life, in K. B. Christensen, M. Mesbah, and S. Kreiner (Eds.), Rasch models for Health Sciences, pp. 197-218.

9. 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 Modeling and Data Analysis, Springer, pp. 65-73.

10. Bartolucci, F. & Scrucca, L. (2010), Point Estimation Methods with Applications to Item Response Theory Models, In: B. McGaw, E. Baker and P. P. Peterson (Editors), International Encyclopedia of Education, 3rd Edition, Elsevier, 7, pp. 366-373.

11. Bartolucci, F., Pennoni, F. & 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.

12. Minozzo, M., Forcina, A. & Bartolucci, F. (2006). Marginal models and pruning of association rules. In: Metodi, Modelli e Tecnologie dell'Informazione a Supporto delle Decisioni, pp. 473-481.

13. Scaccia, L. & Bartolucci, F. (2005), A Hierarchical Mixture Model for Gene Expression Data, in New Developments in Classification and Data Analysis, (editors: M. Vichi, P. Monari, S. Mignani and A. Montanari), Springer, pp. 267-274 (Extended version of the paper presented at CLADAG 2003).

14. Bartolucci, F., Mira, L. & Scaccia, L. (2003), Answering two biological questions with a latent class model via MCMC applied to capture-recapture data, in Applied Bayesian Statistical Studies in Biology and Medicine, (editors: M. Di Bacco, G. D'Amore and F. Scalfari), Kluwer Academic Publishers, pp. 7-23.

15. Bartolucci, F. & De Luca, G. (2002), Estimation of stochastic volatility models, in Computational Methods in Decision-Making, Economics and Finance (editors: E.J. Kontoghiorghes, B. Rustem and S. Siokos Editors), Kluwer Academic Publishers, pp. 541-556.

LETTERS TO EDITORS, COMMENTS TO ARTICLES, EDITORIALS, BOOK REVIEWS

1. Bartolucci, F. and Pennoni, F. (2024), Book review of "VISSER, I., & SPEEKENBRINK, M. (2022). Mixture and Hidden Markov Models with R. Springer, Cham, CH.", Psychometrika, in press.

2. Gupta, R., Bartolucci, F., Katsikis, V. N., and Patnaik, S. (2023), Recent Advancements in Computational Finance and Business Analytics: Proceedings of the International Conference on Computational Finance and Business Analytics, Springer.

3. Bartolucci, F., Peluso, S., and Mira, A. (2023), Comment on "Causal Inference Under Mis-Specification: Adjustment Based on the Propensity Score", Bayesian Analysis, 18, pp. 683-684. 

4. Bartolucci, F. & Pennoni, F. (2019), Comment on "The Class Of CUB Models: Statistical Foundations, Inferential Issues And Empirical Evidence", Statistical Methods and Applications, 28, pp. 437-439.

5. Bartolucci, F. & Giordani, P. (2017), Editorial: Special section on latent variable models for longitudinal data, Biometrical Journal, 59, pp. 781-782.

6. Bartolucci, F. (2016), Discussion on ‘Statistical Modelling of Citation Exchange Between Statistics Journals’ by Cristiano Varin, Manuela Cattelan and David Firth’, Journal of the Royal Statistical Society - Series A, 179, pp. 37-38.

7. Alfò, M. & Bartolucci, F. (2015), Latent variable models for the analysis of socio-economic data, Introduction to Metron Special Issue, 73, pp. 151-154.

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

9. Bartolucci, F. (2012), On a possible decomposition of the h-index, letter to the Editor of the Journal of the American Society for Information Science and Technology, 63, 2126-212.