Sentence examples for ridge regression approach from inspiring English sources

(2013) incorporate a ridge regression approach to the grid search.

This results in a heteroscedastic ridge regression approach that does not need iterative procedures at all.

It outperformed the homoscedastic ridge regression approach for best linear unbiased prediction in particular for situations with high marker density and strong linkage disequilibrium along the chromosomes, a situation that occurs often in plant breeding populations.

The Wiener filter fitted with ridge regression approaches the accuracy of the 1st order UKF as the number of neurons increases.

The resulting scores are presented in Figure 2. The conclusion from the experiments is that although all estimation methods perform rather well, the centered least squares and centered ridge regression approaches seem to show the best performance.

First, how do more advanced ridge regression approaches perform?

Our objectives were (1) to present novel heteroscedastic ridge regression approaches that improve existing approaches with respect to computational efficiency or accuracy of effect estimates and (2) to demonstrate their properties with computer simulations and with data sets of maize, wheat, and sugar beet.

In this paper, we focus on ridge regression, a penalised regression approach that has been shown to offer good performance in multivariate prediction problems.

As long as N ≪ P, there seems to be little advantage of advanced approaches, such as ridge regression, over the classical GWAS approach [ 61].

However, one thing that remains unchanged even under different specifications of the models that try to explain the accuracy of respective methods is that ridge regression outperforms the repeated simple regression approach in all large scale samples considered.

Ridge regression (RR) is one such penalised regression approach.