Cluster-Weighted Modeling is a flexible statistical framework for modeling local relationships in heterogeneous populations on the basis of weighted combinations of local models. Besides the traditional approach based on Gaussian assumptions, here we consider Cluster Weighted Modeling based on Student-t distributions. In this paper we present an EM algorithm for parameter estimation in Cluster-Weighted models according to the maximum likelihood approach.
An EM Algorithm for the Student-t Cluster-Weighted Modeling
INGRASSIA, Salvatore;
2012-01-01
Abstract
Cluster-Weighted Modeling is a flexible statistical framework for modeling local relationships in heterogeneous populations on the basis of weighted combinations of local models. Besides the traditional approach based on Gaussian assumptions, here we consider Cluster Weighted Modeling based on Student-t distributions. In this paper we present an EM algorithm for parameter estimation in Cluster-Weighted models according to the maximum likelihood approach.File in questo prodotto:
| File | Dimensione | Formato | |
|---|---|---|---|
|
Ingrassia_Minotti_Incarbone (2012)_StudiesInClassification.pdf
solo gestori archivio
Licenza:
Non specificato
Dimensione
225.56 kB
Formato
Adobe PDF
|
225.56 kB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
