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Utilizza questo identificativo per citare o creare un link a questo documento:
http://hdl.handle.net/10761/1383
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Data: | 13-mar-2013 |
Autori: | Incarbone, Giuseppe |
Titolo: | 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. |
In | Area 13 - Scienze economiche e statistiche
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