@misc{10481/89674,
year = {2019},
month = {1},
url = {https://hdl.handle.net/10481/89674},
abstract = {In a linear regression, the coefficient of determination, R2, is a relevant measure that represents the percentage of variation in the dependent variable that is explained by a set of independent variables. Thus, it measures the predictive ability of the estimated model. For an ordinary least squares (OLS) estimator, this coefficient is calculated from the decomposition of the sum of squares. However, when the model presents collinearity problems (a strong linear relation between the independent variables), the OLS estimation is unstable, and other estimation methodologies are proposed, with the ridge estimation being the most widely applied. This paper shows that the decomposition of the sum of squares is not verified in the ridge regression and proposes how the coefficient of determination should be calculated in this case.},
publisher = {Taylor and Francis},
keywords = {Multicolinealidad},
keywords = {Multicollinearity},
keywords = {Ridge Regression},
keywords = {Goodness of fit},
keywords = {Sum of squares decomposition},
keywords = {Transformation of variables},
title = {The coefficient of determination in the ridge regression},
doi = {10.1080/03610918.2019.1649421},
author = {Rodríguez Sánchez, Ainara and Salmerón Gómez, Román and García García, Catalina},
}