New method improves accuracy of ridge estimators in linear regression models.
Researchers have developed new methods to improve ridge estimators in linear regression models when dealing with multicollinearity and heteroscedasticity. By introducing a scaling factor, they found that these new estimators outperform traditional ones, especially in cases of severe multicollinearity and heteroscedasticity. The new approach has been tested and shown to be effective in real-life data analysis scenarios with collinear predictors and heteroscedastic errors.
