Statistical algorithms for Cluster Weighted Models

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.

Grantor dc:publisher

Università degli studi di Catania

Year dc:date

2012

Author dc:creator

• INCARBONE, GIUSEPPE

Contributors dc:contributor

• INGRASSIA, Salvatore
• GRECO, Salvatore

• Cluster Weighted Models, Mixture Models, R computing environment

Statement dc:rights

• info:eu-repo/semantics/openAccess
• license:PUBBLICO - Pubblico con Copyright
• license uri:iris.PUB02

Language dc:language

eng

Handle dc:identifier

https://hdl.handle.net/20.500.11769/587599

OAI identifier oai:identifier

oai:www.iris.unict.it:20.500.11769/587599