Giuliano Galimberti;Angela Montanari, Discussion of “Model-based clustering and classification with non-normal mixture distributions” by S.X. Lee and G.J. McLachlan, «STATISTICAL METHODS & APPLICATIONS», 2013, 22, pp. 463 - 465 [replica/breve intervento]
Manrique Rosa; Targetti Stefano; Viaggi Davide; Galimberti Giuliano; Raggi Meri., PAC e gestione del paesaggio: quali gli effetti di secondo ordine?, «AGRIREGIONIEUROPA», 2013, 34, pp. 60 - 63 [articolo]
Galimberti G.; Soffritti G., Using conditional independence for parsimonious model-based Gaussian clustering, «STATISTICS AND COMPUTING», 2013, 23, pp. 625 - 638 [articolo]
Fontanesi L;Schiavo G;Galimberti G;Calò DG;Scotti E;Martelli PL;Buttazzoni L;Casadio R;Russo V, A genome wide association study for backfat thickness in Italian Large White pigs highlights new regions affecting fat deposition including neuronal genes., «BMC GENOMICS», 2012, 13, Article number: 583, pp. 1 - 9 [articolo]Open Access
Galimberti G.; Soffritti G.; Di Maso M., Classification Trees for Ordinal Responses in R: The rpartScore Package, «JOURNAL OF STATISTICAL SOFTWARE», 2012, 47 (Issue 10), pp. 1 - 25 [articolo]
Fontanesi L;Galimberti G;Calò DG;Fronza R;Martelli PL;Scotti E;Colombo M;Schiavo G;Casadio R;Buttazzoni L;Russo V, Identification and association analysis of several hundred single nucleotide polymorphisms within candidate genes for back fat thickness in Italian Large White pigs using a selective genotyping approach., «JOURNAL OF ANIMAL SCIENCE», 2012, 90, pp. 2450 - 2464 [articolo]
G. Galimberti; G. Soffritti, Tree-based methods and decision trees, in: Modern Analysis of Customer Surveys: with applications using R, CHICHESTER, John Wiley & Sons, 2012, pp. 283 - 307 [capitolo di libro]
Fontanesi L.; Galimberti G.; Calò D.G.; Colombo M.; Astolfi A.; Formica S.; Russo V., Microarray gene expression analysis of porcine skeletal muscle sampled at several post mortem time points, «MEAT SCIENCE», 2011, 88, pp. 604 - 609 [articolo]
G. Soffritti; G. Galimberti, Multivariate linear regression with non-normal errors: a solution based on mixture models, «STATISTICS AND COMPUTING», 2011, 21, pp. 523 - 536 [articolo]
G. Galimberti; M. Pillati; G. Soffritti, Notes on the robustness of regression trees against skewed and contaminated errors, in: New Perspectives in Statistical Modeling and Data Analysis, BERLIN, Springer-Verlag, «STUDIES IN CLASSIFICATION, DATA ANALYSIS, AND KNOWLEDGE ORGANIZATION», 2011, pp. 255 - 263 (atti di: 7th Conference of the Classification and Data Analysis Group of the Italian Statistical Society, Catania, Italy, September 9-11, 2009) [Contributo in Atti di convegno]
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]
