Publications

In  Scientific Journals

1. Zaccaria G, García-Escudero, L.A., Greselin, F., Mayo-Iscar, A., 2025, Cellwise outlier detection in heterogeneous populations, Technometrics, DOI: 10.1080/00401706.2025.2497822

2. Vasiljev T., Salvioni L., Colombo M., Galli P., Greselin F., 2025, From animal testing to in Silico models: a systematic review and practical guide to cosmetic assessment. Statistical Methods & Applications, https://doi.org/10.1007/s10260-025-00794-0

3. Greselin, F. and  Zaccaria G., 2024, Studying Hierarchical Latent Structures in Heterogeneous Populations with Missing Information, Journal of Classification,  SCOPUS 2-s2.0-85205530411, WOS:001326005500001

4. Brazauskas, V., Greselin, F., Zitikis, R., 2024 Measuring income inequality via percentile relativities, Quality and Quantity, OpenAccess, SCOPUS s2.0-85192225928 

5. Divincenzo D., Greselin F., Piacenza F., Zitikis R., 2023, A text analysis for Operational Risk loss descriptions, The Journal of Operational Risk, 18(3), 63-90, SCOPUS 2-s2.0-85182326875, WOS:001105157500004

6. Greselin F., Jedrzejczak A., Trzcińska K., 2023, A new parametric approach to Gender Gap, with application to EUSILC data in Poland and Italy, Statistical Analysis and Data Mining: The ASA Data Science Journal,  16(4), 319-335, SCOPUS 2-s2.0-85159179571, WOS:000988628800001

7. Comotti A., Fattori A., Greselin F., Bordini L., Brambilla P., Bonzini M., 2023, Psychometric Evaluation of GHQ-12 as a Screening Tool for Psychological Impairment of Healthcare Workers Facing COVID- 19 Pandemic. La Medicina del Lavoro | Work, Environment and Health, 114, 1, SCOPUS 2-s2.0-85148249844, WOS:000992581500001

8. Cappozzo A., García-Escudero, L.A., Greselin, F., Mayo-Iscar, A., 2022, Graphical and computational tools to guide parameter choice for the cluster weighted robust model, Journal of Computational and Graphical Statistics, 32:3, 1195-1214, SCOPUS 2-s2.0-85146723152, WOS:000909539200001

9. Denti F., Cappozzo A., and Greselin  F.,  2021,  A Two-Stage Bayesian Nonparametric Model for Novelty Detection with Robust Prior Information, Statistics and Computing, 31, 42, SCOPUS 2-s2.0-85106872286,  WOS:000654258400001

10. Cappozzo, A., Duponchel, L., Greselin, F., Murphy, B., 2021, Robust variable selection in the framework of classification with label noise and outliers: applications to spectroscopic data in agri-food, Analytica Chimica Acta, 1153, 338245, SCOPUS 2-s2.0-85101658998, WOS:000635606500001

11. Cappozzo, A.; García-Escudero, L.A.; Greselin, F.; Mayo-Iscar, A., 2021, Parameter Choice, Stability and Validity for Robust Cluster Weighted Modeling, Stats, 4, 602- 615, WOS:000836811900001

12. Cappozzo, A., Greselin, F., Murphy, B., 2021, Robust variable selection for model-based learning in presence of adulteration, Computational Statistics and Data Analysis, 158, 107186, SCOPUS 2-s2.0-85100533727, WOS:000632325800001

13. Greselin, F., Murphy, B., Porzio, G.C., Vistocco, D., 2021, CLADAG 2019 Special Issue: Selected Papers on Classification and Data Analysis, Statistical Analysis and Data Mining: The ASA Data Science Journal, 14 (4), 295–296, SCOPUS 2-s2.0-85108120914, WOS:000662050900001

14. Davydov Y., Greselin F., 2020, Inferential results for a new inequality curve, Mathematical Methods of Statistics, 30 (1-2), 1-15, SCOPUS 2-s2.0-85130961820, WOS:000805587900001

15. Greselin F., Jedrzejczak A., 2020, Analyzing the Gender Gap in Poland and Italy, and by Regions, International Advances in Economic Research, 26, 433-447, SCOPUS  2-s2.0-85097179407, WOS:000599028500001

16. Greselin F., Pellegrino S., Vernizzi A., 2020, The Zenga Equality curve: a new approach to measuring tax redistribution and progressivity, The Review of Income and Wealth, 67(4): 950-976,  SCOPUS 2-s2.0-85097763098,  WOS:000600003300001

17. Cappozzo A., Greselin F., Murphy B., 2020, Anomaly and Novelty detection for robust semi-supervised learning,  Statistics and Computing,  30, 1545-1571.
    (DOI:10.1007/s11222-020-09959-1; SCOPUS 2-s2.0-85087416358, WOS:000544527600001)

18. Cappozzo, A., Greselin, F., Murphy, B., 2020,  A robust approach to model-based classification based on trimming and constraints, Advances in Data Analysis and Classification, 14, 327-354, SCOPUS 2-s2.0-85070929493, WOS:000544712900005

19. Davydov Y., Greselin F., 2020, Comparisons between poorest and richest to measure inequality, Sociological Methods and Research, 49, 2, 526-561, SCOPUS s2.0-85081389009; WOS:000526381000008

20. Greselin, F., Piacenza, F., Zitikis, R., 2019, Practice Oriented and Monte Carlo Based Estimation of the Value-at-Risk for Operational Risk Measurement, Risks, 7(2):50, SCOPUS 2-s2.0-85066936679,  WOS:000474937700016

21. Davydov Y., Greselin F., 2019, Inferential results for a new measure of inequality, The Econometrics Journal, 22(2),  153-172, SCOPUS s2.0-85076514370, WOS:000493351000004

22. García-Escudero L.A., Greselin F.,   Mayo-Iscar A.,  2018, Robust fuzzy and parsimonious clustering based on mixtures of Factor Analyzers, International Journal of Approximate Reasoning, 94, 60-75, SCOPUS 2-s2.0-85041456836; WOS:000426330500005

23. Greselin F.,  Zitikis R., 2018, From the classical Gini index of income inequality to a new Zenga-type relative measure of risk: a modeller's perspective,  Econometrics, 6, 4, SCOPUS s2.0-85056784458; WOS:000441896200003

24. García-Escudero L.A., Gordaliza A., Greselin F.,   Ingrassia S., Mayo-Iscar A., 2018, Eigenvalues and constraints in mixture modeling:  geometric and computational issues,  Advances in Data Analysis and Classification, 94, 60-75, SCOPUS s2.0-85031941854; WOS:000439171100003

25. Giudici, P., Greselin, F., Murphy, B. and Rampichini, C., 2017, CLADAG 2015 special issue: Selected papers on classification and data analysis. Statistical Analysis and Data Mining: The ASA Data Science Journal, 10: 5, SCOPUS 2-s2.0-85010902677; WOS:000393981800001

26. García-Escudero L.A., Gordaliza A., Greselin F.,  Ingrassia S., Mayo-Iscar A., 2017, Robust estimation of mixtures of regressions with random covariates, via trimming and constraints,  Statistics and Computing, 27, 2, 377-402, SCOPUS 2-s2.0-84957579574; WOS:000395004300006

27. García-Escudero L.A., Gordaliza A., Greselin F.,  Ingrassia S., Mayo-Iscar A., 2016, The joint role of  trimming and constraints in robust estimation of Gaussian factor analyzers, Computational Statistics and Data Analysis, 99, 131-147, SCOPUS 2-s2.0-84958177299; WOS:000373653700011

28. Greselin F., Ingrassia S., 2015, Maximum likelihood estimation in constrained parameter spaces for mixtures of Factor Analyzers,  Statistics and Computing, 25, 2, 215-226, SCOPUS 2-s2.0-84886955202; WOS:000349551100004

29. Greselin F., Pasquazzi L., 2015, Parametric vs non-parametric inference on Zenga index of inequality: issues and evidence from survey data, Communications in Statistics - Simulation and Computation, 44, 1702-1719, SCOPUS 2-s2.0-84926386661; WOS:000352721400005

30. Greselin F.,  2014, More equal and  poorer, or richer but more unequal?,  Economic Quality Control, 29,  2, 99--117

31. Greselin F., Pasquazzi L., Zitikis R.,  2014, Heavy tailed capital incomes: Zenga index, statistical inference, and ECHP data analysis, Extremes, 17, 1, 127-155, SCOPUS  2-s2.0-84896543045, WOS:000333122900006

32. Bagnato L., Greselin F., Punzo A., 2014, On the Spectral Decomposition in Normal Discriminant Analysis, Communications in Statistics - Simulation and Computation, 43, 6, 1471-1489, SCOPUS 2-s2.0-84893098424; WOS:000330106100012

33. Greselin F., Punzo A., 2013, Closed Likelihood-Ratio Testing Procedures to Assess Similarity of Covariance Matrices, The American Statistician,  67, 3, 117-128, SCOPUS 2-s2.0-84890187858; WOS:000325911700001

34. Greselin F., Pasquazzi L., Zitikis R., 2012, Contrasting the Gini and Zenga indices of economic inequality, Journal of Applied Statistics,  40, 2, 282-297, SCOPUS 2-s2.0-84871221317; WOS:000312341300004

35. Fattore M., Maggino F., Greselin F., 2011, Socio-economic evaluation with ordinal variables: integrating counting and poset approaches, Statistics & Applications, Special Issue: Partial Orders in Applied Sciences, 31-42. 

36. Greselin F., Ingrassia S., Punzo A., 2011, Assessing the pattern of covariance matrices via an augmentation multiple testing procedure, Statistical Methods and Applications, 20, 2, 141-170, SCOPUS 2-s2.0-79957957239; WOS:000291173000002

37. Greselin F., Pasquazzi L., Zitikis R., 2010, Zenga's new index of economic inequality, its estimation, and an analysis of incomes in Italy, Journal of Probability and Statistics, 26 pp., SCOPUS 2-s2.0-84859192884; WOS: 12153779

38. Greselin F., Ingrassia S., 2010, Constrained monotone EM algorithms for mixtures of multivariate t-distributions,  Statistics and Computing, 20, 1, 9-22, SCOPUS 2-s2.0-73549091028; WOS:000273403900002

39. Greselin F., Puri M.L., Zitikis R., 2009, L-functions, processes, and statistics in measuring economic inequality and actuarial risks,  Statistics and Its Interface, 2, 227-245, WOS:000282649700014

40. Greselin F., Pasquazzi L., 2009, Asymptotic Confidence Intervals for a New Inequality Measure, Communications in Statistics - Simulation and Computation, 38, 8, 1742-1756, SCOPUS 2-s2.0-70449626294;  WOS:000270912500013

41. Greselin F., Pasquazzi L., 2008, Minimum sample sizes in asymptotic confidence intervals for Gini's  inequality measure,  Statistics & Applications, 6, 2, 99-116, SCOPUS 2-s2.0-85018266169

42. Greselin F., Maffenini W., 2007, Minimum sample sizes for confidence intervals for Gini's  mean difference: a new approach for their determination, Statistics & Applications, 5, 1, 103-122, SCOPUS 2-s2.0-85087416358

43. Greselin F., Zenga M., 2006, Convergence of the Sample Mean Difference to the normal distribution: simulation results,  Statistics & Applications, 4, 1, 97-120, SCOPUS 2-s2.0-85100784906

44. Greselin F., 2006, Dependence measures based on partial and total orderings, Statistics & Applications, 4, 2, 3-25, SCOPUS 2-s2.0-8508712417

45. Greselin F., Zenga M., Polisicchio M., 2004,  The variance of Gini's  mean difference and its estimators, Statistica, 3,  455-475.

46. Greselin F., Zenga M., 2004,  A partial ordering of dependence for contingency tables, Statistics & Applications, 2, 53-71, SCOPUS 2-s2.0-85087116867

47. Greselin F., Zenga M., 2004, Partial and total orderings of dependence on tables with given margins, Quaderni di Statistica, 6,  129-155.

48. Greselin F., 2003, Counting and enumerating frequency tables with given margins, Statistics & Applications, 1, 2, 87-104, SCOPUS2-s2.0-79952799111

49. Greselin F., 1997,  Valutazione approssimata della distorsione dello stimatore R^ nella distribuzione di Pareto, Quaderni di Statistica e Matematica Applicata alle Scienze Economico-Sociali, 19, 3, 269-279. 

50. Bellassai, G., Cernuzzi, L., Greselin, F., 1993, Teaching software engineering for the development of Paraguay, In: Software Engineering Education. Elsevier, 129-134, SCOPUS: 2-s2.0-0027755522, WOS:A1993BZ97G00015

Books, Editorship and Curatorships

51. Cazzaro M., Greselin F., Modelli statistici per l'analisi economica e finanziaria.  with Mylab platform, eText and online updates, 138 pages, Pearson Italy - Milano, ISBN 9788891903419

52. Greselin F.,  Mola F.,  Zenga Ma. Editors, Cladag 2017 Book of Short Papers, eBook, Universitas Studiorum, ISBN: 978-88-99459-71-0

53. Greselin F.,  Deldossi L., Bagnato L.,and Vichi M. Editors, Statistical Learning of Complex Data, Springer Series Studies in Classification, Data Analysis, and Knowledge Organization, ISBN 978-3-030-21140-0

54. Porzio G.C., Greselin F.,  and Balzano S. Editors,  Book of Short PapersCladag 2019, eBook, Universitas Studiorum, ISBN 978-88-8317-108-6

Publications in volumes with ISBN

55. Brazauskas, V., Greselin, F., Zitikis, R., 2024, Median-Based Inequality Measures. In: Pollice, A., Mariani, P. (eds) Methodological and Applied Statistics and Demography III. SIS 2024. Italian Statistical Society Series on Advances in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-031-64431-3_47

56. Di Vincenzo D., Greselin F., Piacenza F., Zitikis R., 2024, A Tweet Data Analysis for Detecting Emerging Operational Risks. In: Corazza, M., Gannon, F., Legros, F., Pizzi, C., Touzé, V. (eds) Mathematical and Statistical Methods for Actuarial Sciences and Finance. MAF 2024. Springer, Cham

57. Greselin F. and Zaccaria G., 2023, Handling missing data in complex phenomena: an ultrametric model-based approach for clustering, pp 961-966, in Book of short papers - SIS 2023, Pearson, ISBN: 9788891935618. 

58. Cappozzo A., García-Escudero L.A., Greselin F., Mayo-Iscar A., 2022,  Monitoring Tools in Robust CWM for the Analysis of Crime Data, in Building Bridges between Soft and Statistical Methodologies for Data Science, vol. 1433 of the Springer series Advances in Intelligent Systems and Computing, Springer, DOI:10.1007/978-3- 031-15509-3, ISBN: 978-3-031-15508-6

59. Denti F., Cappozzo A., Greselin F., 2022, Outlier and Novelty Detection for Functional Data: a Semiparametric Bayesian Approach, pp. 42, in Classification and Data Science in the Digital Age - Book of Abstracts IFCS 2022 , Printed by Instituto Nacional de Estatística, ISBN: 978-989-98955-9-1

60. Cappozzo A., García-Escudero L.A, Greselin F., Mayo-Iscar A., 2022,  Monitoring Hyperparameter Choice for Robust Cluster Weighted Model, pp. 105,  in Classification and Data Science in the Digital Age - Book of Abstracts IFCS 2022, Printed by Instituto Nacional de Estatística, ISBN: 978-989-98955-9-1

61. Comotti A. and Greselin F., 2022, Robustifying the Rasch model with the forward search, in SIS2022 Book of the Short Papers, Balzanella A., Bini M., Cavicchia C. and Verde R. Editors, pp.1693-1698, Pearson Ed., ISBN: 9788891932310

62. Cappozzo A., García-Escudero L.A, Greselin F., Mayo-Iscar A., 2021, Exploring solutions via monitoring for cluster-weighted robust models, in ClaDAG 2021 Book of Abstracts and Short papers, Firenze University Press, 2021, G.C.Porzio, C. Rampichini, and C. Bocci Editors, pp. 284–287, doi:10.36253/978-88-5518-340-6, e-ISBN: 978-88-5518-340-6

63. Greselin F. and Jedrzejczak A., 2021, Quantifying the impact of covariates on the gender gap measurement: an analysis based on EU-SILC data from Poland and Italy, in ClaDAG 2021 Book of Abstracts and Short papers, Firenze University Press, 2021, G.C.Porzio, C. Rampichini, and C. Bocci Editors, pp. 108–111, doi:10.36253/978-88- 5518-340-6, e-ISBN: 978-88-5518-340-6

64. Cappozzo A., Duponchel L., Greselin F., Murphy T.,  2021, Robust classification of spectroscopic data in agri-food: first analysis on the stability of results, in ClaDAG 2021 Book of Abstracts and Short papers, Firenze University Press, 2021, G.C.Porzio, C. Rampichini, and C. Bocci Editors, pp. 49–52, doi:10.36253/978-88-5518-340-6, e-ISBN: 978-88-5518-340-6

65. Denti F., Cappozzo A., and Greselin F., 2021, Outlier and novelty detection for Functional data: a semi-parametric Bayesian approach, in Short Papers in Models and Learning in Clustering and Classification, S. Ingrassia, A. Punzo, R. Rocci Editors, pp. 33–38, LEDIpublishing, ISBN: 9788855265393.

66. Cappozzo A., and Greselin F., 2021, Monitoring tools for Cluster Weighted Robust Models, in Short papers SIS 2021, Pearson Eds., 2021, pp. 1245–1250, C. Perna, N. Salvati and F. Schirripa Spagnolo Editors, ISBN 9788891927361

67. Cappozzo A., Greselin F., and Murphy B., 2021, Robust Model-Based Learning to Discover New Wheat Varieties and Discriminate Adulterated Kernels in X-Ray Images in Statistical Learning and Modeling in Data Analysis Springer Berlin Heidelberg, 2021, pp. 29–36. Springer Series “Studies in Classification, Data Analysis, and Knowledge Organization”, S. Balzano, G.C. Porzio, R. Salvatore, D. Vistocco, M. Vichi Editors, ISBN: 978- 303069943-7 

68. Denti F., Cappozzo A., and Greselin  F., 2020, Bayesian nonparametric adaptive classification with robust prior information, in Book of short Papers SIS 2020, Milan: Pearson, 2020, pp. 655-660, A. Pollice, N. Salvati and F. Schirripa Spagnolo Editors, ISBN 9788891910776

69. Cappozzo A.,  Greselin F., and  Murphy B., 2020, Variable selection for robust model-based learning from contaminated data, in Book of short Papers SIS 2020, Milan: Pearson, 2020, pp. 1117-1120, A. Pollice, N. Salvati and F. Schirripa Spagnolo Editors, ISBN: 9788891910776

70. Cappozzo, A, Greselin F., and Murphy  B., 2019, Supervised learning in presence of outliers, label noise and unobserved classes. in: Book of short papers | Cladag2019, Centro Editoriale di Ateneo Università di Cassino e del Lazio Meridionale, pp. 104-107,  ISBN: 978-88-8317-108-6 

71. Cappozzo, A., Greselin F., Manzi G., 2019, Predicting and improving smart mobility: a robust model-based approach to the BikeMi BSS. in: Smart Statistics for Smart Applications 2019 - Book of Short papers, Milan, Pearson, pp. 737-742, ISBN 9788891915108 

72. Cappozzo, A., and Greselin  F., 2019, Detecting wine adulterations employing robust mixture of factor analyzers. In: Statistical Learning of Complex Data. Springer Berlin Heidelberg, pp. 13-21. Springer Series  Studies in Classification, Data Analysis, and Knowledge Organization, ISBN  978-3-030-21140-0 (SCOPUS 2-s2.0-85072854538)

73. García-Escudero L.A, Greselin F.,  Mayo-Iscar A., 2018, Extending robust fuzzy clustering to skew data, CFE-CMStatistics 2018 Book of Abstracts, p. 26, London. Technical Editors: Ana Colubi, Erricos J. Kontoghiorghes and Herman K. Van Dijk, 2018, ISBN  978-9963-2227-5-9.

74. Cappozzo, A., Greselin F.,  Murphy T., 2018, The role of trimming and variable selection in robust model-based classification for food authenticity studies. In COMPSTAT 2018 Book of Abstracts, p. 35, COMPSTAT 2018 and CRoNoS FDA 2018, Ana Colubi and Cristian Gatu Editors,  ISBN 978-9963-2227-3-5.

75. García-Escudero L.A, Greselin F.,  Mayo-Iscar A., 2018, Fuzzy clustering of multivariate skew data. In COMPSTAT 2018 Book of Abstracts, p. 33, Ana Colubi and Cristian Gatu Editors, ISBN 978-9963-2227-3-5

76. Cappozzo A., Greselin F.,  Murphy T., 2018,  Robust Updating Classification Rule with applications in Food Authenticity Studies, in A. Abbruzzo, E. Brentari, M. Chiodi, & D. Piacentino (a cura di), Book of short Papers SIS 2018, Pearson, ISBN  9788891910233.

77. García-Escudero L.A, Greselin F., Agustin Mayo-Iscar A., McLachlan G., 2017, Advances in robust estimation of skew normal mixtures.  CFE-CMStatistics 2017 Book of Abstracts, p. 120, London. Technical Editors: Gil Gonzalez-Rodriguez and Marc Hofmann, 2017, ISBN 978-9963-2227-4-2.

78. García-Escudero L., Gordaliza A., Greselin F., Ingrassia S., Mayo-Iscar A., 2017, To get the best, tame the beast: constrained ML estimation for mixture models. Book of abstracts for the "III International Workshop on Proximity Data, Multivariate Analysis and Classification",  2017, ISBN 978-84-697-7418-2

79. García-Escudero L., Gordaliza A., Greselin F., Mayo-Iscar A., 2018, Robust approaches for fuzzy clusterwise regression based on trimming and constraints, in The Mathematics of the Uncertain: A Tribute to Pedro Gil, Eduardo Gil, Eva Gil, Juan Gil and Maria Angeles Gil Editors, Springer Series: Studies in Systems, Decision and Control, Springer International Publishing, 2017, ISBN 978-3-319-73847-5 ( SCOPUS 2-s2.0-85042905192)

80. García-Escudero L., Greselin F., Mayo-Iscar A., 2017, New proposals for clustering based on trimming and restrictions, in Cladag 2017 Book of Short Papers, Editors Francesca Greselin, Francesco Mola and Mariangela Zenga, Universitas Studiorum S.r.l., 2017, ISBN 978-88-99459-71-0.

81. Cappozzo A., Greselin F., 2017,  Wine authenticity assessed via trimming, in Cladag 2017 Book of Short Papers, Editors Francesca Greselin, Francesco Mola and Mariangela Zenga, Universitas Studiorum S.r.l., ISBN 978-88-99459-71-0.

82. García-Escudero L., Greselin F., Mayo-Iscar A., 2016, Fuzzy Clustering through Robust Factor Analyzers, in Soft Methods for Data Science, Eds. M.B. Ferraro, P. Giordani, B. Vantaggi, M. Gagolewski, M. A. Gil, P. Grzegorzewski, and O. Hryniewicz, Springer International Publishing, pp. 229-235,  ISBN 978-3-319-42972-4, SCOPUS s2.0-84988422083; WOS: 000390837600029

83. García-Escudero L., Greselin F., Mayo-Iscar A., McLachlan G., 2016, Robust estimation of mixtures of Skew Normal Distributions.  Proceedings of the 48th Scientific Meeting of the Italian Statistical Society,   Eds. Monica Pratesi and Cira Perna, Dipartimento di Scienze Economiche e Statistiche (publisher), Fisciano (SA, Italy), ISBN 9788861970618.

84. García-Escudero L.A, Greselin F., Agustin Mayo-Iscar A., McLachlan G., 2016, Robust estimation of mixture models with skew components via trimming and constraints. CFE-CMStatistics 2016 Book of Abstracts, p. 6, Sevilla. Technical Editors: Angela Blanco-Fernandez and Gil Gonzalez-Rodriguez, 2016, ISBN 978-9963-22271-1.

85. García-Escudero L., Greselin F., Mayo-Iscar A., A fuzzy version of robust mixtures of Gaussian factor analyzers. CFE-CMStatistics 2016 Book of Abstracts, p. 5, Sevilla. Technical Editors: Angela Blanco-Fernandez and Gil Gonzalez-Rodriguez, ISBN 978-9963-2227-1-1.

86. García-Escudero L., Greselin F., Mayo-Iscar A., McLachlan G., 2015, Robust estimation for mixtures of skew data. Proceedings of the 8th International Conference of the ERCIM WG on Computing & Statistics (CMStatistics 2015), ISBN 978-9963-2227-0-4.

87. García-Escudero L.,  Gordaliza A., Greselin F., Ingrassia S.,  Mayo-Iscar A., 2015, Robust estimation for mixtures of Gaussian factor analyzers. Proceedings of the 8th International Conference of the ERCIM WG on Computing & Statistics (CMStatistics 2015), ISBN 978-9963-2227-0-4.

88. Garcìa-Escudero L., Greselin F., Mayo-Iscar A., 2015, Robust clustering for heterogenous skew data. Book of Abstracts, 10th International Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, CUEC Editrice by Sardinia Novamedia, Cagliari,  ISBN 978-88-8467-949-9

89. García-Escudero L.,  Gordaliza A., Greselin F., Ingrassia S., Mayo-Iscar A., 2014, Robust model estimation, through trimming and constraints,   for  mixtures of Factor Analyzers.  Proceedings of the 7th International Conference of the ERCIM WG on Computing & Statistics (ERCIM'14),  ISBN 978-84-937822-4-5.

90. García-Escudero L.,  Gordaliza A., Greselin F., Ingrassia S., Mayo-Iscar A., 2014,  An adaptive method to robustify ML estimation in Cluster Weighted Modeling. Proceedings of the 47th Scientific Meeting of the Italian Statistical Society, CUEC  Eds., Cagliari (Italy), ISBN 978 88 8467 874 4

91. Greselin F., Ingrassia S., 2013, Data-driven EM constraints for Gaussian mixtures of Factor Analyzers, Book of Abstracts, 9th International Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, Cleup Eds., Modena (Italy),  p. 245-248, ISBN 978 88 6787 117 9.

92. Greselin F., Ingrassia S., 2013, Market segmentation via mixtures of constrained factor analyzers. Advances in Latent Variables, Eds. Brentari E., Carpita M.,  Vita e Pensiero, Milan (Italy) , 8 pages, ISBN 978 88 343 2556 8.

93. Greselin F., Ingrassia S., 2012, Constrained EM algorithms for Gaussian mixtures of Factor Analyzers.   Proceedings of the 5th International Conference of the ERCIM WG on Computing & Statistics (ERCIM'12), Oviedo (Spain), ISBN 978 84 937822 2 1.

94. Greselin F., Punzo A., 2012, Closed Likelihood-Ratio Testing Procedures to Assess Similarity of Covariance Matrices.  Proceedings of the 46th Scientific Meeting of the Italian Statistical Society, Cleup Eds., Rome (Italy), ISBN 978 88 6129 882 8

95. Greselin F., Pasquazzi L., Zitikis R., 2012, Capital income inequality: evidences from ECHP data. Proceedings of the 46th Scientific Meeting of the Italian Statistical Society, Cleup Eds., Rome (Italy), ISBN 978 88 6129 882 8

96. Corbett B., Greselin F., Pasquazzi L., Zitikis R., Williams R.,  2012, Not all measures of income inequality are equal: A comparison between the Gini and the Zenga.   Proceedings of the International Interdisciplinary Conference, IISES Eds., Palermo (Italy), ISBN 978-80-905241-0-1.

97. Bagnato L., Greselin F.,  2011, Model-based clustering and classification via patterned covariance analysis. Book of Short Papers of the 8th International Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, ClaDAG 2011,  Pavia University Press, Pavia (Italy), 4 pages, ISBN 978-88-96764-22-0.

98. Greselin F., Pasquazzi L., 2010, Dagum Confidence intervals for inequality measures. Proceedings of the 45th Scientific Meeting of the Italian Statistical Society, Cleup Eds., Padua (Italy), ISBN 978-88-6129-566-7.

99. Greselin F., Pasquazzi L., Zitikis R., 2010, Asymptotic theory for Zenga's  new index of economic inequality. Proceedings of the 45th Scientific Meeting of the Italian Statistical Society,  Cleup Eds., Padua (Italy), ISBN 978-88-6129-566-7.

100. Greselin F., Ingrassia S., 2010,  Weakly homoscedastic constraints for mixtures of t-distributions.  Advances in Data Analysis, Data Handling and Business Intelligence,  A.Fink, B.Lausen, W.Seidel and A.Ultsch editors, Springer Berlin Heidelberg, p. 219-228,  ISBN 978-364201043-9, SCOPUS 2-s2.0-84879590606, WOS:000282669800020

101. Greselin F., Ingrassia S., Punzo A.,  2009, Multivariate tests for patterned covariance matrices. Book of Short Papers of Cladag 2009,   CLEUP Eds., Padova (Italy), p. 529-532, ISBN 978-88-6129-406-6.

102. Greselin F., Pasquazzi L., 2008, Asymptotic Confidence Intervals for a New Inequality Measure. Proceedings of the 44th Scientific Meeting of the Italian Statistical Society, Cleup Eds., Padua (Italy), ISBN  978-88-6129-228-4.

103. Greselin F., Pasquazzi L., 2007, Minimum Sample Sizes in Asymptotic Confidence Intervals for Gini's  Concentration Index. ISI 2007 Book of abstract,  M.I. Gomes, D. Pestana,P. Silva. Eds, CEAUL INE and ISI, Lisbon (Portugal), ISBN 978-972-8859-71-8.

104. Greselin F., 2005, New results on a partial ordering of dependence. Classification and Data Analysis, S. Zani and A. Cerioli editors, MUP ed., Parma (Italy), p. 377-380, ISBN 978-88-7847-066-8. 

105. Greselin F., Zenga M., 2002, Measures of association in the Fréchet class. Proceedings of the 41th Scientific Meeting of the Italian Statistical Society,  Cleup Eds., Milan (Italy), p. 33-36, ISBN 88-7178-589-4

106. Greselin F., Bellassai G., Cernuzzi L., 1993, Teaching Software Engineering for the development of Paraguay. Proceedings of the IFIP WG3.4/SEARCC (SRIG on Education and Training),  Hong Kong, North Holland, p. 129-134, ISBN 0-444-81597-X, SCOPUS 2-s2.0-0027755522; WOS:A1993BZ97G00015