Choice of the ridge factor from the correlation matrix determinant García García, Claudia Salmerón Gómez, Román García García, Catalina Multicolinealidad Essential multicollinearity Ridge Regression Ridge regression is the alternative method to ordinary least squares, which is mostly applied when a multiple linear regression model presents a worrying degree of collinearity. A relevant topic in ridge regression is the selection of the ridge parameter, and different proposals have been presented in the scientific literature. Since the ridge estimator is biased, its estimation is normally based on the calculation of the mean square error (MSE) without considering (to the best of our knowledge) whether the proposed value for the ridge parameter really mitigates the collinearity. With this goal and different simulations, this paper proposes to estimate the ridge parameter from the determinant of the matrix of correlation of the data, which verifies that the variance inflation factor (VIF) is lower than the traditionally established threshold. The possible relation between the VIF and the determinant of the matrix of correlation is also analysed. Finally, the contribution is illustrated with three real examples. 2024-02-29T08:17:23Z 2024-02-29T08:17:23Z 2018 info:eu-repo/semantics/article Published version: Claudia García, Román Salmerón Gómez & Catalina B. García (2019). Choice of the ridge factor from the correlation matrix determinant. Journal of Statistical Computation and Simulation, 89:2, 211-231. https://doi.org/10.1080/00949655.2018.1543423 https://hdl.handle.net/10481/89664 10.1080/00949655.2018.1543423 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivatives 4.0 Internacional Taylor and Francis