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Please use this identifier to cite or link to this item: http://hdl.handle.net/10761/1383

Issue Date: 13-Mar-2013
Authors: Incarbone, Giuseppe
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. 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.
Appears in Collections:Area 13 - Scienze economiche e statistiche

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