Collinearity: revisiting the variance inflation factor in ridge regression

By Catalina Beatriz García García, José García, M. M. L. López Martín, Román Salmerón. 2015

231 citations recorded by the source corpus.

Ridge regression has been widely applied to estimate under collinearity by defining a class of estimators that are dependent on the parameter k. The variance inflation factor (VIF) is applied to detect the presence of collinearity and also as an objective method to obtain the value of k in ridge regression. Contrarily to the definition of the VIF, the expressions traditionally applied in ridge regression do not necessarily lead to values of VIFs equal to or greater than 1. This work presents an alternative expression to calculate the VIF in ridge regression that satisfies the aforementioned condition and also presents other interesting properties.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3b863e7cba3f7a54fdd36e417ff1aa2ba236e6b5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

Citations in the corpus

Standardization of Variables and Collinearity Diagnostic in Ridge Regression (2016, background): “Recently, Garcia et al. (2015) established that the correct extension of the VIF to RE has to be carried out from the model YR D XRˇ C uR.”
Collinearity diagnostic applied in ridge estimation through the variance inflation factor (2016, background): “[12] extended the VIF for the case p=2 and the condition number was extended by García et al.”