Galimberti G.; Soffritti G., Finite mixture models for clustering multilevel data with multiple cluster structures, «STATISTICAL MODELLING», 2010, 10(3), pp. 265 - 290 [articolo]
G. Soffritti; G. Galimberti, Detecting multiple cluster structures through model-based clustering methods, in: Classification and Data Analysis 2009, PADOVA, CLEUP, 2009, pp. 263 - 266 (atti di: 7° Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, Catania, Italy, September 9-11 2009) [Contributo in Atti di convegno]
G. Cavrini; G. Galimberti; G. Soffritti, Evaluating patient satisfaction through latent class factor analysis, «HEALTH & PLACE», 2009, 15, pp. 210 - 218 [articolo]
M. Di Martino; G. Galimberti; G. Soffritti, Evaluating public services through multivariate linear regression analysis, «QUADERNI DI STATISTICA», 2009, 11, pp. 183 - 201 [articolo]
G. Galimberti; A. Montanari; C. Viroli, Penalized factor mixture analysis for variable selection in clustered data, «COMPUTATIONAL STATISTICS & DATA ANALYSIS», 2009, 53, pp. 4301 - 4310 [articolo]
G. Galimberti; M. Pillati; G. Soffritti, Some issues on robustness of regression trees, in: Classification and Data Analysis 2009, PADOVA, CLEUP, 2009, pp. 147 - 150 (atti di: 7° Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, Catania, Italy, 9-11 Settembre 2009) [Contributo in Atti di convegno]
Fontanesi L.; Fronza R.; Scotti E.; Colombo M.; Speroni C.; Tognazzi L.; Galimberti G.; Calò DG; Bonora E.; Vargiolu M.; Romeo G.; Casadio R.; Russo V., The FAGenomicH project: Towards a whole candidate gene approach to identify markers associated with fatness and production traits in pigs and investigate the pig as a model for human obesity, «ITALIAN JOURNAL OF ANIMAL SCIENCE», 2009, 8, pp. 87 - 89 [articolo]Open Access
G. Galimberti; A. Montanari; C. Viroli, latent Classes of Objects and Variable Selection, in: COMPSTAT 2008 - Proceedings in Computational Statistics, HEIDELBERG, Physica-Verlag Springer, 2008, pp. 373 - 383 (atti di: COMPSTAT 2008 - 18th Conference of IASC-ERS, Porto - Portugal, 24-29 Agosto 2008) [Contributo in Atti di convegno]
G. Galimberti; M. Pillati; G. Soffritti, Robust regression tree-based methods, in: AUTORI VARI, First joint meeting of the Société Francophone de Classification and the Classification and Data Analysis Group of the Italian Statistical Society, NAPOLI, Edizioni Scientifiche Italiane, 2008, pp. 305 - 308 (atti di: Joint meeting of the Société Francophone de Classification and the Classification and Data Analysis Group of the Italian Statistical Society, Caserta, Italy, 11-13 luglio 2008) [Contributo in Atti di convegno]
G. Galimberti; M. Pillati; G. Soffritti, Comparing strategies for robust regression tree construction, in: Classification and Data Analysis 2007, MACERATA, Edizioni Università di Macerata, 2007, pp. 355 - 358 (atti di: Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, Macerata, 12-14 Settembre 2007) [Contributo in Atti di convegno]
G. Galimberti; G. Soffritti, Model-based methods to identify multiple cluster structures in a data set, «COMPUTATIONAL STATISTICS & DATA ANALYSIS», 2007, 52, pp. 520 - 536 [articolo]
G. Galimberti; G. Soffritti, Multiple cluster structures and mixture models: recent developments for multilevel data, in: Classification and Data Analysis 2007, MACERATA, Edizioni Università di Macerata, 2007, pp. 203 - 206 (atti di: Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, Macerata, 12-14 Settembre 2007) [Contributo in Atti di convegno]
G. Galimberti; M. Pillati; G. Soffritti, Robust regression trees based on M-estimators, «STATISTICA», 2007, 2, pp. 173 - 190 [articolo]
M. Costa; G. Galimberti; A. Montanari, Binary segmentation methods based on Gini Index: a new approach to the multidensional analysis of income inequalities, «STATISTICA & APPLICAZIONI», 2006, IV numero speciale 1, pp. 19 - 37 [articolo]
G. Galimberti; G. Soffritti, Identifying multiple cluster structures through latent class models, in: From Data and Information Analysis to Knowledge Engineering, BERLIN, Springer, 2006, pp. 174 - 181 (Studies in Classification, Data Analysis, and Knowledge Organization) [capitolo di libro]
