(Main) Peer-Reviewed Articles


2023

• Di Mari R., Ingrassia S. and Punzo A. (2023).
  Local and overall deviance R-squared measures for mixtures of generalized linear models.
  Journal of Classification, (forthcoming).

• Corrente S., Ingrao C., Punzo A. and Matarazzo A. (2023).
  Evaluating citizens' satisfaction on the urban environmental management through a multi-criteria approach: An application experience in Sicily.
  Environmental Impact Assessment Review, 99, 107029.

2022

• Cremaschini A., Punzo A., Martellucci E. and Maruotti A. (2022).
  On stylized facts of cryptocurrencies returns and their relationship with other assets, with a focus on the impact of COVID-19.
  Applied Economics incorporating Applied Financial Economics, (forthcoming).

• Punzo A. and Bagnato L. (2022).
  Multiple scaled symmetric distributions in allometric studies.
  The International Journal of Biostatistics, 18(1), 219–242.

• Gallaugher M. P. B., Tomarchio S. D., McNicholas P. D. and Punzo A. (2022).
  Multivariate Cluster Weighted Models Using Skewed Distributions.
  Advances in Data Analysis and Classification, 16(1), 93–124.

• Merlo L., Maruotti A., Petrella L. and Punzo A. (2022).
  Quantile hidden semi-Markov models for multivariate time series.
  Statistics and Computing, 32, 61.

• Tomarchio S. D., Punzo A. and Maruotti A. (2022).
  Parsimonious Hidden Markov Models for matrix-variate longitudinal data.
  Statistics and Computing, 32, 53.

• Gallaugher M. P. B., Tomarchio S. D., McNicholas P. D. and Punzo A. (2022).
  Model-based clustering via skewed matrix-variate cluster-weighted models.
  Journal of Statistical Computation and Simulation, 92(13), 2645-2666.

• Punzo A. and Bagnato L. (2022).
  Dimension-wise scaled normal mixtures with application to finance and biometry.
  Journal of Multivariate Analysis, 191, 105020.

• Tomarchio S. D., Bagnato L. and Punzo A. (2022).
  Model-based clustering via new parsimonious mixtures of heavy tailed distributions.
  AStA Advances in Statistical Analysis, 106(2), 315-347.

• Bagnato L., Punzo A. and Zoia M. G. (2022).
  Leptokurtic moment-parameterized elliptically contoured distributions with application to financial stock returns.
  Communications in Statistics - Theory and Methods, 51(2), 486-500.

• Tomarchio S. D., Gallaugher M. P. B., Punzo A. and McNicholas P. D. (2022).
  Mixtures of Matrix-Variate Contaminated Normal Distributions.
  Journal of Computational and Graphical Statistics, 31(2), 413-421.

2021

• Di Mari R., Dotto F., Farcomeni A. and Punzo A. (2021).
  Assessing Measurement Invariance for Longitudinal Data through Latent Markov Models.
  Structural Equation Modeling: A Multidisciplinary Journal, 29(3), 381-393.

• Tomarchio S. D., McNicholas P. D. and Punzo A. (2021).
  Matrix Normal Cluster-Weighted Models.
  Journal of Classification, 38(3), 556-575.

• Maruotti A. and Punzo A. (2021).
  Initialization of hidden Markov and semi-Markov models: a critical evaluation of several strategies.
  International Statistical Review, 89(3), 447-480.

• Punzo A. and Tortora C. (2021).
  Multiple scaled contaminated normal distribution and its application in clustering.
  Statistical Modelling, 21(4), 332–358.

• Bagnato L. and Punzo A. (2021).
  Unconstrained representation of orthogonal matrices with application to common principal components.
  Computational Statistics, 36(2), 1177–1195.

• Punzo A., Ingrassia S. and Maruotti A. (2021).
  Multivariate hidden Markov regression models: random covariates and heavy-tailed distributions.
  Statistical Papers, 62(3), 1519–1555.

• Punzo A. and Bagnato L. (2021).
  The multivariate tail-inflated normal distribution and its application in finance.
  Journal of Statistical Computation and Simulation, 91(1), 1–36.

• Punzo A. and Bagnato L. (2021).
  Modelling the cryptocurrency return distribution via Laplace scale mixtures.
  Physica A: Statistical Mechanics and its Applications, 563(1), 125354.

2020

• Farcomeni A. and Punzo A. (2020).
  Robust model-based clustering with mild and gross outliers.
  TEST, 29(4), 989–1007.

• Punzo A. and Bagnato L. (2020).
  Allometric analysis using the multivariate shifted exponential normal distribution.
  Biometrical Journal, 62(6), 1525–1543.

• Tomarchio S. D., Punzo A. and Bagnato L. (2020).
  Two new matrix-variate distributions with application in model-based clustering.
  Computational Statistics & Data Analysis, 152, 107050.

• Tomarchio S. D. and Punzo A. (2020).
  Dichotomous unimodal compound models: Application to the distribution of insurance losses.
  Journal of Applied Statistics, 47(13–15), 2328–2353.

• Ingrassia S. and Punzo A. (2020).
  Cluster validation for mixtures of regressions via the total sum of squares decomposition.
  Journal of Classification, 37(2), 526–547.

• Di Mari R., Bakk Z. and Punzo A. (2020).
  A random-covariate approach for distal outcome prediction with latent class analysis.
  Structural Equation Modeling: A Multidisciplinary Journal, 27(3): 351–368.

• Mazza A. and Punzo A. (2020).
  Mixtures of multivariate contaminated normal regression models.
  Statistical Papers, 61(2), 787–822.

• Punzo A., Blostein M. and McNicholas P. D. (2020).
  High-dimensional unsupervised classification via parsimonious contaminated mixtures.
  Pattern Recognition, 98, 107031.

2019

• Tomarchio S. D. and Punzo A. (2019).
  Modelling the loss given default distribution via a family of zero-and-one inflated mixture models.
  Journal of the Royal Statistical Society: Series A, 182(4), 1247–1266.

• Zarei S., Mohammadpour A., Ingrassia S. and 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, 43(3), 1059–1069.

• Punzo A. (2019).
  A new look at the inverse Gaussian distribution with applications to insurance and economic data.
  Journal of Applied Statistics, 46(7), 1260–1287.

• 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.

• 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.

2018

• 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., Mazza A. and Maruotti A. (2018).
  Fitting insurance and economic data with outliers: A flexible approach based on finite mixtures of contaminated gamma distributions.
  Journal of Applied Statistics, 45(14), 2563–2584.

• 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.

• Punzo A., Bagnato L. and Maruotti A. (2018).
  Compound unimodal distributions for insurance losses.
  Insurance: Mathematics and Economics, 81, 95–107.

• Bagnato L., De Capitani L. and Punzo A. (2018).
  Testing for serial independence: Beyond the Portmanteau approach.
  The American Statistician, 72(3), 219–238.

• Punzo A., Ingrassia S. and 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.

2017

• Punzo A. and McNicholas P. D. (2017).
  Robust clustering in regression analysis via the contaminated Gaussian cluster-weighted model.
  Journal of Classification, 34(2), 249–293.

• Maruotti A. and Punzo A. (2017).
  Model-based time-varying clustering of multivariate longitudinal data with covariates and outliers.
  Computational Statistics & Data Analysis, 113, 475–496.

• 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.

• Dang U. J., Punzo A., McNicholas P. D., Ingrassia S. and Browne R. P. (2017).
  Multivariate response and parsimony for Gaussian cluster-weighted models.
  Journal of Classification, 34(1), 4–34.

• 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.

2016

• Berta P., Ingrassia S., Punzo A. and Vittadini G. (2016).
  Multilevel cluster-weighted models for the evaluation of hospitals.
  METRON, 74(3), 275–292.

• Punzo A. and Maruotti A. (2016).
  Clustering multivariate longitudinal observations: The contaminated Gaussian hidden Markov model.
  Journal of Computational and Graphical Statistics, 25(4), 1097–1116.

• Punzo A. and McNicholas P. D. (2016).
  Parsimonious mixtures of multivariate contaminated normal distributions.
  Biometrical Journal, 58(6), 1506–1537.

• Bagnato L., De Capitani L. and Punzo A. (2016).
  The Kullback-Leibler autodependogram.
  Journal of Applied Statistics, 43(14), 2574–2594.

• Mazza A. and Punzo A. (2016).
  Spatial attraction in migrants' settlement patterns in the city of Catania.
  Demographic Research, 35(5), 117–138.

• Maruotti A., Punzo A., Mastrantonio G. and Lagona F. (2016).
  A time-dependent extension of the projected normal regression model for longitudinal circular data based on a hidden Markov heterogeneity structure.
  Stochastic Environmental Research and Risk Assessment, 30(6), 1725–1740.

• Punzo A. and Ingrassia S. (2016).
  Clustering bivariate mixed-type data via the cluster-weighted model.
  Computational Statistics, 31(3), 989–1013.

• Punzo A., Browne R. P. and McNicholas P. D. (2016).
  Hypothesis testing for mixture model selection.
  Journal of Statistical Computation and Simulation, 86(14): 2797–2818.

• Ingrassia S. and Punzo A. (2016).
  Decision boundaries for mixtures of regressions.
  Journal of the Korean Statistical Society, 45(2): 295–306.

2015

• Subedi S., Punzo A., Ingrassia S. and McNicholas P. D. (2015).
  Cluster-weighted t-factor analyzers for robust model-based clustering and dimension reduction.
  Statistical Methods & Applications, 24(4): 623–649.

• 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.

• 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.

• Mazza A. and Punzo A. (2015).
  On the upward bias of the dissimilarity index and its corrections.
  Sociological Methods & Research, 44(1): 80–107.

2014

• 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.
  AStA 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.

2013

• Punzo A. and Ingrassia S. (2013).
  On the use of the generalized linear exponential cluster-weighted model to assess local linear independence in bivariate data.
  QdS - Journal of Methodological and Applied Statistics, 15: 131–144.

• 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.

2012

• 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.

• 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.

2011

• Greselin F., Ingrassia S. and Punzo A. (2011).
  Assessing the pattern of covariance matrices via an augmentation multiple testing procedure.
  Statistical Methods & Applications, 20(2): 141–170.

2010

• Bagnato L. and Punzo A. (2010).
  On the Use of χ²-Test to Check Serial Independence.
  Statistica & Applicazioni, VIII(1): 57–74.

2009

• Punzo A. (2009).
  On Kernel Smoothing in Polytomous IRT: A New Minimum Distance Estimator.
  Quaderni di Statistica, 11: 15–37.

• Bagnato L. and Punzo A. (2009).
  Nonparametric Bootstrap Test for Autoregressive Additive Models.
  Statistics in Transition, 10(3), 359–370.

Book Chapters

• Punzo A. and Tomarchio S. D. (2022). Parsimonious finite mixtures of matrix-variate regressions. In: Bekker A., Ferreira J. T., Arashi M., Chen D. G. (Eds.), Innovations in Multivariate Statistical Modeling, Emerging Topics in Statistics and Biostatistics, pp. 385-398, Cham, Switzerland: Springer.

• Di Mari R., Ingrassia S. and Punzo A. (2021). A generalized coefficient of determination for mixtures of regressions. In: Chadjipadelis T., Lausen B., Markos A., Lee T. R., Montanari A., Nugent R. (Eds.), Data Analysis and Rationality in a Complex World, Studies in Classification, Data Analysis, and Knowledge Organization, pp. 27-35, AG, Switzerland: Springer Nature.

• 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., Bagnato L., Vichi M. (Eds.), Statistical Learning of Complex Data, Studies in Classification, Data Analysis, and Knowledge Organization, pp. 121-131, Switzerland: Springer International Publishing.

• 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, vol. 288, pp. 373-391, Switzerland: Springer Nature.

• 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.

• 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.

• 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. 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.

• 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.