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EnglishA mixture of regression models for multivariate observed variables which contextually involves a dimension reduction step through a linear factor model is proposed. The model estimation is performed via the EM-algorithm and a procedure to compute asymptotic standard errors for the parameter estimates is developed. The proposed approach is applied to the study of students satisfaction towards different aspects of their school as a function of various covariates.
| Titolo: | Dimensionally reduced mixtures of regression models |
| Autore/i: | MONTANARI, ANGELA; VIROLI, CINZIA |
| Autore/i Unibo: | |
| Anno: | 2011 |
| Rivista: | |
| Digital Object Identifier (DOI): | 10.1016/j.jspi.2010.11.024 |
| Abstract: | A mixture of regression models for multivariate observed variables which contextually involves a dimension reduction step through a linear factor model is proposed. The model estimation is performed via the EM-algorithm and a procedure to compute asymptotic standard errors for the parameter estimates is developed. The proposed approach is applied to the study of students satisfaction towards different aspects of their school as a function of various covariates. |
| Data prodotto definitivo in UGOV: | 2013-05-06 16:05:53 |
| Appare nelle tipologie: | 1.01 Articolo in rivista |
File in questo prodotto:
http://hdl.handle.net/11585/96300
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