New method improves accuracy of linear model predictions, outperforming traditional approach.
The article presents a new method called universal least squares for linear models. This method improves upon traditional least squares by not needing a specific design matrix. It combines ridge and least squares estimates. The study shows that the universal least squares method outperforms traditional least squares. The researchers also discuss how to choose the balance parameter for this method. Overall, the study demonstrates the effectiveness of the universal least squares method through an application example.
