Publications

Main Publications

• 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

• Bagnato L., De Capitani L and Punzo A. (2018). Testing for serial independence: Beyond the Portmanteau approach. The American Statistician, 72(3), 219-238.
• Di Mari R. and Bakk Z. (2018). Mostly harmless direct effects: a comparison of different latent Markov modeling approaches. Structural Equation Modeling: A Multidisciplinary Journal, 25(3), 467-483.
• Garcia-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,12(2), 203-233.
• Mazza A.,Punzo A., and Ingrassia S. (2018). flexCWM: A Flexible Framework for Cluster-Weighted Models, Journal of Statistical Software, 86(2), 1-30
• Punzo A., Bagnato L., and Maruotti A. (2018). Compound unimodal distributions for insurance losses, Insurance: Mathematics and Economics, 81, 95–107. DOI: https://dx.doi.org/10.1016/j.insmatheco.2017.10.007
• Punzo A., Ingrassia S., Maruotti A., (2018). Multivariate generalized hidden Markov regression models with random covariates: physical exercise in an elderly population, Statistics in Medicine, 37(19), 2797-2808
• Punzo A., Mazza A. and McNicholas P. D. (2018). ContaminatedMixt: An R Package for Fitting Parsimonious Mixtures of Multivariate Contaminated Normal Distributions, Journal of Statistical Software, 85(10): 1–25. DOI: https://dx.doi.org/10.18637/jss.v085.i10
• Rocci R., Gattone S.A., and Di Mari R. (2017). A data driven equivariant approach to constrained Gaussian mixture modeling Advances in Data Analysis and Classification, 12(2), 235-260.

• Bagnato L., Punzo A. and Zoia M. G. (2017). The multivariate leptokurtic-normal distribution and its application in model-based clustering, Canadian Journal of Statistics, 45(1), 95–119
• Bagnato L., De Capitani L. and Punzo A. (2017). A diagram to detect serial dependencies: an application to transport time series, Quality & Quantity, 51(2), 581–594.
• Bagnato L., De Capitani L. and Punzo A. (2017). Testing for serial independence: Beyond the Portmanteau approach, The American Statistician, (forthcoming).
• Dang U. J., Punzo A., McNicholas P.D., Ingrassia S., Browne R. P. (2017). Multivariate response and parsimony for Gaussian cluster-weighted models, Journal of Classification, 34(1), 4–34.
• Di Mari R., Rocci R., and Gattone S.A. (2017). Finite mixture of linear regression model: an adaptive constrained approach to maximum likelihood estimation. In: "Ferraro M. et al (Eds.), Soft Methods for Data Science. Advances in Intelligent System and Computing", vol. 456, Springer, Switzerland.
• Fossati L., Marcelja S. E., Staab D., Cubillos P. E., France K., Haswell C. A., Ingrassia S., Jenkins J. S., Koskinen T., Lanza A. F., Redfield S., Youngblood A., Pelzmann G. (2017). The effect of ISM absorption on stellar activity measurements and its relevance for exoplanet studies, Astronomy & Astrophysics, 601, A104.
• Garcia-Escudero L.A., Gordaliza A., Greselin F., Ingrassia S., Mayo-Iscar A. (2016). Robust estimation of mixtures of regressions with random covariates, via trimming and constraints, Statistics and Computing, 27(2), 377-402.
• Maruotti A. and Punzo A. (2017). Model-based time-varying clustering of multivariate longitudinal data with covariates and outliers. Computational Statistics & Data Analysis, (forthcoming).
• Mazza A. and Punzo A. (2017). Dealing with omitted answers in a survey on social integration of immigrants in Italy. Mathematical Population Studies, 24(2), 84–102.
• Punzo A., Mazza A. and McNicholas P. D. (2017). ContaminatedMixt: An R Package for Fitting Parsimonious Mixtures of Multivariate Contaminated Normal Distributions. Journal of Statistical Software, (forthcoming).
• Punzo A. and McNicholas P. D. (2017). Robust clustering in regression analysis via the contaminated Gaussian cluster-weighted model. Journal of Classification, 34(2), (forthcoming).
• Di Mari R., Rocci R., and Gattone S.A. (2017). Clusterwise linear regression modeling with soft scale constraints. International Journal of Approximate Reasoning, 91, 160-178.

• Berta P., Ingrassia S., Punzo A., Vittadini G. (2016), Multilevel cluster-weighted models for the evaluation of hospitals, Metron, 74(3): 275-292.
• Di Mari R., Oberski D.L., and Vermunt J.K. (2016). Bias-adjusted three step latent Markov modeling with covariates. Structural Equation Modeling: A Multidisciplinary Journal, 23(5), 649-660.
• Garcia-Escudero L.A., Gordaliza A., Greselin F., Ingrassia S., Mayo-Iscar A. (2016). Robust estimation of mixtures of regressions with random covariates, via trimming and constraints, Statistics and Computing, DOI 10.1007/s11222-016-9628-3, forthcoming.
• Garcia-Escudero L.A., Gordaliza A., Greselin F., Ingrassia S., Mayo-Iscar A. (2016). The joint role of trimming and constraints in robust estimation for mixtures of Gaussian factor analyzers, Computational Statistics & Data Analysis, 99: 131-147.
• Ingrassia S., Punzo A. (2016). Decision boundaries for mixtures of regressions, Journal of the Korean Statistical Society, 45(2): 295-306.
• Punzo A. and Ingrassia S. (2016) Clustering Bivariate Mixed-Type Data via the Cluster-Weighted Model, Computational Statistics, 31(3): 989-1013, DOI 10.1007/s00180-015-0600-z
• Punzo A. and Maruotti A. (2016). Clustering multivariate longitudinal observations: The contaminated Gaussian hidden Markov model, Journal of Computational and Graphical Statistics, 25(4).

• Bagnato L., De Capitani L., Mazza A. and Punzo A. (2015). SDD: An R Package for Serial Dependence Diagrams. Journal of Statistical Software, 64(Code Snippet 2): 1–19.
• Fossati L., Ingrassia S. nD Lanza A.F. (2015). A bimodal correlation between host star chromospheric emission and the surface gravity of hot-Jupiters, The Astrophysical Journal Letters, 812 (2): L35.
• Greselin F. and  Ingrassia S. (2015). Maximum likelihood estimation in constrained parameter spaces for mixtures of factor analyzers, Statistics and Computing, 25(2): 215-226.
• Ingrassia S., Punzo A.,Vittadini G., and Minotti S.C.(2015).The Generalized Linear Mixed Cluster-Weighted Model, Journal of Classification, 32(1): 85-113.
• Mazza A. and Punzo A. (2015). Bivariate Discrete Beta Kernel Graduation of Mortality Data. Lifetime Data Analysis, 21(3): 419–433.
• Mazza A. and Punzo A. (2015). On the upward bias of the dissimilarity index and its corrections. Sociological Methods and Research, 44(1): 80–107.
• Subedi S., Punzo A., Ingrassia S., McNicholas P.D. (2015). Cluster-Weighted t-Factor Analyzers for Robust Model-Based Clustering and Dimension Reduction, Statistical Methods and Applications, 24(4): 623-649.
• Punzo A. and Ingrassia S. (2015). Parsimonious Generalized Linear Gaussian Cluster-Weighted Models. In: Morlini I., Minerva T., Vichi M. (Eds.), Advances in Statistical Models for Data Analysis, Studies in Classification, Data Analysis, and Knowledge Organization, pp. 201-209, Switzerland: Springer International Publishing.

• Bagnato L., De Capitani L. and Punzo A. (2014). Testing Serial Independence via Density-Based Measures of Divergence, Methodology and Computing in Applied Probability, 16(3): 627–641.
• Mazza A., Punzo A. and McGuire B. (2014). KernSmoothIRT: An R Package for Kernel Smoothing in Item Response Theory. Journal of Statistical Software, 58(6): 1–34.
• Punzo A. (2014). Flexible Mixture Modeling with the Polynomial Gaussian Cluster-Weighted Model, Statistical Modelling, 14(3): 257–291.
• Mazza A. and Punzo A. (2014). DBKGrad: An R Package for Mortality Rates Graduation by Fixed and Adaptive Discrete Beta Kernel Techniques, Journal of Statistical Software, 57(Code Snippet 2): 1–18.
• Bagnato L., De Capitani L. and Punzo A. (2014). Detecting Serial Dependencies with the Reproducibility Probability Autodependogram. Advances in Statistical Analysis, 98(1): 35–61.
• Bertoli-Barsotti L. and Punzo A. (2014). Refusal to Answer Specific Questions in a Survey: A Case Study. Communications in Statistics - Theory and Methods, 43(4): 826–838.
• Ingrassia S., Minotti S. C. and Punzo A. (2014). Model-Based Clustering Via Linear Cluster-Weighted Models. Computational Statistics & Data Analysis, 71(4): 159–182.
• Bagnato L., Greselin F. and Punzo A. (2014). On the Spectral Decomposition in Normal Discriminant Analysis. Communications in Statistics - Simulation and Computation, 43(6): 1471–1489.
• Bertoli-Barsotti L., Lando T. and Punzo A. (2014). Estimating a Rasch Model via Fuzzy Empirical Probability Functions. In: Vicari D., Okada A., Ragozini G., Weihs C. (Eds.), Analysis and Modeling of Complex Data in Behavioural and Social Sciences, Studies in Classification, Data Analysis, and Knowledge Organization, pp. 29-36, Switzerland: Springer International Publishing.

• 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 - Journal of Methodological and Applied Statistics, 15, 131-144.
• Riggi S., Ingrassia S. (2013). A model-based clustering approach for mass composition analysis of high energy cosmic rays, Astroparticle Physics, 48, 86-96.
• Bertoli-Barsotti L. and Punzo A. (2013). Rasch Analysis for Binary Data with Nonignorable Nonresponses. Psicológica, 34(1): 97–123.
• Subedi S., Punzo A., Ingrassia S. and McNicholas P. D. (2013). Clustering and Classification via Cluster-Weighted Factor Analyzers. Advances in Data Analysis and Classification, 7(1): 5–40.
• Bertoli-Barsotti L. and Punzo A. (2013). Modelling missingness with a Rasch-type model. Annales del'I.S.U.P., 57(1–2): 29–44.
• Bagnato L. and Punzo A. (2013). Finite mixtures of unimodal beta and gamma densities and the k-bumps algorithm. Computational Statistics, 28(4): 1571–1597.
• Greselin F. and Punzo A. (2013). Closed Likelihood Ratio Testing Procedures to Assess Similarity of Covariance Matrices. The American Statistician, 67(3): 117–128.
• Mazza A. and Punzo A. (2013). Graduation by Adaptive Discrete Beta Kernels. In: Giusti A., Ritter G., Vichi M. (Eds.), Classification and Data Mining, Studies in Classification, Data Analysis, and Knowledge Organization, pp. 243–250, Berlin Heidelberg: Springer-Verlag.
• Bagnato L. and Punzo A. (2013). Using the Autodependogram in Model Diagnostic Checking. In: Pesarin F., Torelli N., Bar-Hen A. (Eds.), Advances in Theoretical and Applied Statistics, Studies in Theoretical and Applied Statistics, pp. 129–139, Berlin Heidelberg: Springer-Verlag.
• Mazza A. and Punzo A. (2013). Using the Variation Coefficient for Adaptive Discrete Beta Kernel Graduation. In: Giudici P., Ingrassia S., Vichi M. (Eds.), Statistical Models for Data Analysis, Studies in Classification, Data Analysis, and Knowledge Organization, pp. 225–232, Switzerland: Springer International Publishing.

• Bagnato L., Punzo A. and Nicolis O. (2012). The Autodependogram: A Graphical Device to Investigate Serial Dependences. Journal of Time Series Analysis, 33(2): 233–254.
• Ingrassia S., Minotti S.C., Vittadini G. (2012). Local Statistical Modeling via a Cluster-Weighted Approach with Elliptical Distributions, Journal of Classification, 29(3), 363-401.
• Punzo A. and Zini A. (2012). Discrete Approximations of Continuous and Mixed Measures on a Compact Interval. Statistical Papers, 53(3): 563–575.
• Bertoli-Barsotti L. and Punzo A. (2012). Comparison of two bias reduction techniques for the Rasch model. Electronic Journal of Applied Statistical Analysis, 5(3): 360–366.
• 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.
• 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.

• Greselin F., Ingrassia S. and Punzo A. (2011). Assessing the pattern of covariance matrices via an augmentation multiple testing procedure. Statistical Methods and Applications, 20(2): 141–170.
• Ingrassia S. and Rocci R. (2011). Degeneracy of the EM algorithm for the MLE of multivariate Gaussian mixtures and dynamic constraints, Computational Statistics and Data Analysis, 55, 1715-1725

• Bagnato, L. and Punzo A. (2010). On the Use of ­χ²-Test to Check Serial Independence. Statistica & Applicazioni, VIII(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

• 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.