In this paper a mixture of regression models for multivariate ob- served variables which contextually involves a dimension reduction step through a linear factor model is proposed. The model estimation is performed via the EM-algorithm, which also allows to test the significance of the regression coefficients. The proposed approach is applied to the study of students satisfaction towards university courses as function of various covariates.

Dimensionally reduced mixtures of regression models / A. Montanari; C. Viroli. - ELETTRONICO. - (2006). (Intervento presentato al convegno IASC - INTERFACE - IFCS workshop tenutosi a Anacapri, Italy nel 04-06 September).

Dimensionally reduced mixtures of regression models

MONTANARI, ANGELA;VIROLI, CINZIA
2006

Abstract

In this paper a mixture of regression models for multivariate ob- served variables which contextually involves a dimension reduction step through a linear factor model is proposed. The model estimation is performed via the EM-algorithm, which also allows to test the significance of the regression coefficients. The proposed approach is applied to the study of students satisfaction towards university courses as function of various covariates.
2006
Knowledge Extraction and Modelling
Dimensionally reduced mixtures of regression models / A. Montanari; C. Viroli. - ELETTRONICO. - (2006). (Intervento presentato al convegno IASC - INTERFACE - IFCS workshop tenutosi a Anacapri, Italy nel 04-06 September).
A. Montanari; C. Viroli
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/28953
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