The VIF and MSE in Raise Regression
Metadatos
Afficher la notice complèteEditorial
MDPI
Materia
Detection Mean square error Multicollinearity Raise regression Variance inflation factor
Date
2020-04Referencia bibliográfica
Salmerón Gómez, R.; Rodríguez Sánchez, A.; García, C.G.; García Pérez, J. The VIF and MSE in Raise Regression. Mathematics 2020, 8, 605. [doi:10.3390/math8040605]
Résumé
The raise regression has been proposed as an alternative to ordinary least squares estimation
when a model presents collinearity. In order to analyze whether the problem has been mitigated,
it is necessary to develop measures to detect collinearity after the application of the raise regression.
This paper extends the concept of the variance inflation factor to be applied in a raise regression.
The relevance of this extension is that it can be applied to determine the raising factor which allows
an optimal application of this technique. The mean square error is also calculated since the raise
regression provides a biased estimator. The results are illustrated by two empirical examples where
the application of the raise estimator is compared to the application of the ridge and Lasso estimators
that are commonly applied to estimate models with multicollinearity as an alternative to ordinary
least squares.