{"id":{"repo_id":"catania","oai_identifier":"oai:www.iris.unict.it:20.500.11769/587599"},"canonical_url":"https://search.dev.ndltd.org/etd/catania/oai:www.iris.unict.it:20.500.11769/587599","repository":{"repo_id":"catania","name":"Università degli Studi di Catania","base_url":"https://www.iris.unict.it/oai/request"},"display":{"title":"Statistical algorithms for Cluster Weighted Models","abstract":"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. 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