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.
A. Montanari, C. Viroli (2006). Dimensionally reduced mixtures of regression models. NAPOLI : Tilapia.
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.File in questo prodotto:
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