2026-09-25T06:34:01Zhttps://www.iris.unict.it/oai/request

oai:www.iris.unict.it:20.500.11769/5875992024-01-19T00:07:51Zcom_20.500.11769_434851com_123456789_40col_20.500.11769_434852

Statistical algorithms for Cluster Weighted Models INCARBONE, GIUSEPPE Incarbone, Giuseppe INGRASSIA, Salvatore GRECO, Salvatore Cluster Weighted Models, Mixture Models, R computing environment Cluster-weighted modeling (CWM) is a mixture approach to modeling the joint probability of data coming from a heterogeneous population. In this thesis first we investigate statistical properties of CWM from both theoretical and numerical point of view for both Gaussian and Student-t CWM. Then we introduce a novel family of twelve mixture models, all nested in the linear-t cluster weighted model (CWM). This family of models provides a unified framework that also includes the linear Gaussian CWM as a special case. Parameters estimation is carried out through algorithms based on maximum likelihood estimation and both the BIC and ICL are used for model selection. Finally, based on these algorithms, a software package for the R language has been implemented. 2012-12-10 info:eu-repo/semantics/doctoralThesis https://hdl.handle.net/20.500.11769/587599 eng info:eu-repo/semantics/openAccess Università degli studi di Catania place:Catania license:PUBBLICO - Pubblico con Copyright license uri:iris.PUB02