Main Publications

2019
  • Zarei S., Mohammadpour A., Ingrassia S., Punzo A. (2019). On the use of the sub-Gaussian α-stable distribution in the Cluster-Weighted Model, Iranian Journal of Science and Technology, Transactions A: Science , DOI:10.1007/s40995-018-0526-8, (forthcoming)
  • Mazza A. and Punzo A. (2019). Mixtures of multivariate contaminated normal regression models, Statistical Papers, (forthcoming). DOI: https://dx.doi.org/10.1007/s00362-017-0964-y
  • Maruotti A., Punzo A. and Bagnato L. (2019). Hidden Markov and semi-Markov models with multivariate leptokurtic-normal components for robust modeling of daily returns series, Journal of Financial Econometrics, 17(1): 91–117. DOI: https://dx.doi.org/10.1093/jjfinec/nby019
  • Morris K., Punzo A., McNicholas P. D. and Browne R. P. (2019). Asymmetric Clusters and Outliers: Mixtures of Multivariate Contaminated Shifted Asymmetric Laplace Distributions, Computational Statistics & Data Analysis, 132, 145–166. DOI: https://doi.org/10.1016/j.csda.2018.12.001
  • Mazza A., Battisti M., Ingrassia S. and Punzo A. (2019). Modeling return to education in heterogeneous populations. An application to Italy. In: "Greselin I., Deldossi L., Vichi M., Bagnato L. (Eds.), Advances in Statistical Models for Data Analysis, Studies in Classification, Data Analysis, and Knowledge Organization, Switzerland: Springer International Publishing, forthcoming.
  • Mazza A. and Punzo A. (2019). Modeling Household Income with Contaminated Unimodal Distributions. In: "Petrucci A., Racioppi F., Verde R. (Eds.), New Statistical Developments in Data Science", Springer Proceedings in Mathematics & Statistics (PROMS), Switzerland: Springer Nature, forthcoming.
  • Punzo A. (2019). A new look at the inverse Gaussian distribution with applications to insurance and economic data, Journal of Applied Statistics, (forthcoming). DOI: https://doi.org/10.1080/02664763.2018.1542668
2018
2017
2016
2015
2014
2013
2012
2011
2010
  • Bagnato, L. and Punzo A. (2010). On the Use of ­χ2-Test to Check Serial IndependenceStatistica & ApplicazioniVIII(1): 57–74.
  • Greselin F., Ingrassia S. (2010). Constrained monotone EM algorithms for mixtures of multivariate t-distributions, Statistics and Computing, 20(1), 9-22
Book Chapters
  • Ingrassia S., Minotti S.C. and Incarbone G. (2012). An EM Algorithm for the Student-t Cluster-Weighted Modeling, in “Gaul W., Geyer-Schulz A., Schmidt-Thieme L., Kunze J. (Eds.), Challenges at the Interface of Data Analysis, Computer Science, and Optimization”, Springer-Verlag, Berlin, 2012, 13-21.
  • Bagnato L. and Punzo A. (2012). Checking Serial Independence of Residuals from a Nonlinear Model. In: Gaul W., Geyer-Schulz A., Schmidt-Thieme L., Kunze J. (Eds.), Challenges at the Interface of Data Analysis, Computer Science, and Optimization, Studies in Theoretical and Applied Statistics, pp. 203–211, Berlin Heidelberg: Springer-Verlag.
  • Mazza A. and Punzo A. (2011). Discrete Beta Kernel Graduation of Age-Specific Demographic Indicators. In: Ingrassia S., Rocci R., Vichi M. (Eds.), New Perspectives in Statistical Modeling and Data Analysis, Studies in Classification, Data Analysis, and Knowledge Organization, pp. 127–134, Berlin Heidelberg: Springer-Verlag.
  • Punzo A. (2010). Considerations on the Impact of Ill-Conditioned Configurations in the CML Approach.. In: Fink A., Lausen B., Seidel W., Ultsch A. (Eds.), Advances in Data Analysis, Data Handling and Business Intelligence, Studies in Classification, Data Analysis, and Knowledge Organization, pp. 563–572, Berlin Heidelberg: Springer-Verlag.
  • Greselin F. and Ingrassia S. (2010). Weakly Homoscedastic Constraints for Mixtures of t-Distributions, in Fink A., Lausen B., Seidel W., Ultsch A. (Eds), Advances in Data Analysis, Data Handling and Business Intelligence, Springer-Verlag, Berlin, 219-228
  • Punzo A. (2010). Discrete Beta-Type Models. In: Locarek-Junge H., Weihs C. (Eds.), Classification as a Tool for Research, Studies in Classification, Data Analysis, and Knowledge Organization, pp. 253–261, Berlin Heidelberg: Springer-Verlag.