Salvatore Ingrassia

Main Publications

• 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.
• 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.
• 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.
• Berta P., Ingrassia S., Punzo A., Vittadini G. (2016), Multilevel cluster-weighted models for the evaluation of hospitals, Metron, 74(3), 275-292.
• Ingrassia S., Punzo A. (2016). Decision boundaries for mixtures of regressions, Journal of the Korean Statistical Society, 45(2), 295-306.
• Punzo A., 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
• 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.
• Fossati L., Ingrassia S., 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.
• 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.
• IngrassiaS.,PunzoA.,VittadiniG.,MinottiS.C.(2015).The Generalized Linear Mixed Cluster-Weighted Model, Journal of Classification, 32(1), 85-113.
• Greselin F., Ingrassia S. (2015). Maximum likelihood estimation in constrained parameter spaces for mixtures of factor analyzers, Statistics and Computing, 25(2), 215-226.
• Ingrassia S., Minotti S.C., Punzo A. (2014). Model-based clustering via linear cluster-weighted models, Computational Statistics & Data Analysis, 71, 159-182. doi: 10.1016/j.csda.2013.02.012
• 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.
• Subedi S., Punzo A., Ingrassia S., McNicholas P.D. (2013). Clustering and Classification via Cluster- Weighted Factor Analyzers, Advances in Data Analysis and Classification, 7(1), 5-40.
• Greselin F., Ingrassia S. (2013). Market segmentation via mixtures of constrained factor analyzers, in "Brentari E., Carpita M. (Eds), Advances in Latent Variables, Vita e Pensiero, Milano, ISBN 9788834325568 (Electronic Book).
• 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.
• Ingrassia S., Minotti S.C., 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. 
• S. Ingrassia S., Rocci R. (2011). Degeneracy of the EM algorithm for the MLE of multivariate Gaussian mixtures and dynamic constraints, Computational Statistics & Data Analysis, 55(4), 1715-1725.
• Greselin F., S. Ingrassia S., Punzo A. (2011). Assessing the pattern of covariance matrices via an aug- mentation multiple testing procedure, Statistical Methods and Applications, 20(2), 141-170.
• Ingrassia S., Rocci R., Vichi M (Eds.) (2011). New Perspectives in Statistical Modeling and Data Analysis, Springer, Heidelberg. 
• Greselin F., Ingrassia S. (2010). Constrained monotone EM algorithms for mixtures of multivariate t- distributions, Statistics and Computing, 20(1), 9-22.
• Ingrassia S., Trinchera L. (2010). Some remarks on nonlinear relationships in PLS Path Modeling, Statistica Applicata, 20(3-4), 197-216.
• Greselin F., 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 
• Ingrassia S., Morlini I. (2009). Computational Studies with the equivalent number of degrees of freedom in Neural Networks, Advances and Applications in Statistics, 13(1), 49-81.
• Cozzucoli P., Ingrassia S., Costanzo G.D., Mazza A. (2008). Indicatori statistici per la valutazione della soddisfazione didattica universitaria, Rivista di Economia e Statistica del Territorio, 3, 77-90.
• Ingrassia S., Rocci R. (2007). Constrained monotone EM algorithms for finite mixture of multivariate Gaussians, Computational Statistics & Data Analysis, 51, 5339-5351.
• Cozzucoli P., Ingrassia S. (2006). Indicatori dinamici di efficienza didattica dei corsi di laurea universitari, Statistica & Applicazioni,  3(1), 61-68.
• Ingrassia S., Rocci R. (2006). Monotone constrained EM algorithms for multinormal mixture models, in “Zani S., Cerioli A., Riani M., Vichi M. (Eds.), Data Analysis, Classification and the Forward Search”,
  Springer-Verlag, Berlin, 111-118. 
• Ingrassia S., Morlini I. (2005). Modeling neural networks for small datasets, Technometrics, 47(3), 297-311.
• Ingrassia S., Costanzo G.D. (2005). Functional principal component analysis of financial time series, in ”M.Vichi, P. Monari, S. Mignani, A. Montanari (Eds.), New Developments in Classification and Data Analysis”,
  Springer-Verlag, 351-358.
• Ingrassia S. (2004). A Likelihood-Based Constrained Algorithm for Multivariate Normal Mixture Models, Statistical Methods and Applications, 13 (2), 151-166.
• Costanzo G.D., Ingrassia S. (2004). Analysis of the MIB30 basket in the period 2000-2002 by functional PC's, in “J. Antoch (Ed.), Proceedings of COMPSTAT 2004 Symposium”, Physica-Verlag, 807-814.
• Ingrassia S., Morlini I. (2004). On the degrees of freedom in richly parameterised models, in “J. Antoch (Ed.), Proceedings of COMPSTAT 2004 Symposium”, Physica-Verlag, 1237-1244.
• Cerioli A., Ingrassia S., Corbellini A. (2004). Classificazione simbolica di dati funzionali: un'applicazione al monitoraggio ambientale, in “C. Lauro & C. Davino (a cura di), Data Mining e Analisi Simbolica”, Franco Angeli Editore, Milano, 2004, 31-64
• S. Ingrassia, A. Cerioli, A. Corbellini (2003). Some Issues on clustering of functional data, in “M. Schader, W. Gaul, and M.Vichi (Eds.), Between Data Science and Applied Data Analysis”, m
• Springer-Verlag, 49-56.
• Ingrassia, C. Davino (a cura di) (2002). Reti Neuronali e Metodi Statistici, Franco Angeli Editore, Milano.
• Ingrassia S., Morlini I. (2002). Modelli neuronali per piccoli insiemi di dati, in “N.C. Lauro & G. Scepi (a cura di), Analisi Multivariata per la Qualità Totale. Metodologia,
  aspetti computazionali ed applicazioni”,Franco Angeli Editore, 29-40.
• Cavarra S., Crupi V., Guglielmino E., Ingrassia S. (2001). Reti Neurali per la rilevazione di anomalie da dati vibrometrici: un caso studio, Statistica Applicata, 13(1), 5-16.
• Domma F., Ingrassia S. (2001). Mixture models for maximum likelihood estimation from incomplete values, in “S. Borra, R. Rocci, M.Vichi and M. Schader (Eds.), Studies in Classification, Data Analysis and Knowkedge Organization”,
  Springer-Verlag, 201-208.
• Gilio A., Ingrassia S. (2000). Extension of totally coherent interval-valued probability assessment, in “B. Bouchon-Meunier, R.R. Yager and L.A. Zadeh (Eds.), Uncertainty in Intelligent and Information Systems”,
  World Scientific, 80-91.
• Ingrassia S. (1999). Geometrical aspects of discrimination by multilayer perceptrons, Journal of Multivariate Analysis, 68, 226-234.
• Ingrassia S. (1999). Logistic discrimination by Kullback-Leibler type distance measures, in “M.Vichi and O.Opitz (Eds.), Classification and Data Analysis”, Springer-Verlag, (1999), 89-96.
• Gilio A., Ingrassia S. (1998). Totally coherent set-valued probability assessments, Kybernetika, 34(1), 3-15.
• Ingrassia S. (1998). A note on the approximation by superposition of sigmoidal functions, in “A. Bellacicco e A. Laforgia (a cura di), Funzioni Speciali e Applicazioni”, Franco Angeli Editore, 57-67.
• Ingrassia S. (1997). On the realization of discriminant functions by means of multilayer perceptrons, Metron, LV (3-4), 185-200.
• Ingrassia S. (1997). Sulle proprietà discriminanti delle trasformazioni sigmoidali, in in “A. Bellacicco e N.C. Lauro (a cura di), Reti Neurali e Statistica”, Franco Angeli Editore, 99-108.
• Ingrassia S., Mammana M.L., Commis E. (1997). Internet in Italia: un’indagine statistica, Annali della Facoltà di Economia dell’Università di Catania, XLI, 175-199.
• Torrisi A., Ingrassia S., et al. (1996). A study of greek pottery and clay statuettes from the votive deposit in the sanctuary of Demetra in Catania, Annali di Chimica, 86, 329-341.
• Ingrassia S., Commis E. (1994). A neural network approach to defect detection in oranges, Le Matematiche, 48(2), 273-286.
• Ingrassia S., Anile A.M., Commis E. (1994). Defect discrimination in citrus via neural network, in “A. Fasano e M. Primicerio (Eds.), Proceedings of the Seventh European Conference on Mathematics in Industry”,
  B.G. Teubner, Stuttgart, 239-246.
• Ingrassia S. (1994). On the rate of convergence of the Metropolis algorithm and the Gibbs sampler by geometric bounds, The Annals of Applied Probability, 4(2), 347-389.
• Ingrassia S. (1993). Geometric approaches to the estimation of the spectral gap of reversible Markov chains, Combinatorics, Probability & Computing, 2(3), 301-323.
• Ingrassia S. (1992). A comparison between the simulated annealing and the EM algorithms in normal mixture decompositions, Statistics and Computing, 2(4), 203-211.
• Ingrassia S. (1991). Mixture decomposition via the simulated annealing algorithm, Applied Stochastic Models and Data Analysis, 7 (4), 317-325

• Direzione aziendale - 1 anno
  METODI STATISTICI PER LE ANALISI DI MERCATO
  
  
• Economia - 2 anno
  STATISTICA

• Direzione aziendale - 1 Year
  STATISTICS FOR MARKETING
  
  
• Economia - 2 Year
  STATISTICS

Mixture