ridge regression analysis

ridge regression analysis

[′rij ri′gresh·ən ə‚nal·ə·səs]
(statistics)
A form of regression analysis in which damping factors are added to the diagonal of the correlation matrix prior to inversion, a procedure which tends to orthogonalize interrelated variables; study of the robustness of the regression coefficients with changes in the damping factors is then used to determine sets of variables that should be removed. Also known as damped regression analysis.
References in periodicals archive ?
A single formula based on C3 and C4 vertebral body heights with different coefficients for each gender was derived using ridge regression analysis.
A single formula with two different coefficients for both genders was formed using ridge regression analysis.
Due to the multicollinearity problem, ridge regression analysis was preferred instead of multivariate linear regression for statistical evaluations to define a model to predict skeletal age based on C3_H and C4_H.
Coefficient selection is an important issue in ridge regression analysis.
For this aim, statistical performances of Stepwise Regression Analysis, use of factor analysis scores with multiple regression model analysis and Ridge Regression analysis were evaluated on data of 131 Balochi male sheep.
Similarly, Ridge regression analysis is a helpful tool to overcome some deficiencies of multiple linear regression analysis in the appearance of multicollinearity problem due to strongly bivariate-correlations between independent variables.
With the purpose of obtaining the best functional model, statistical performances of Stepwise Regression Analysis, use of factor analysis scores with multiple regression model analysis, and Ridge Regression analysis were compared each other.
Ridge Regression Analysis: In ridge regression analysis, the cross-product matrix for the explanatory variables (scrotal circumference, scrotal length, testicular length, body length, withers height and heart girth) is centered and scaled to one on the diagonal elements.
In the Ridge regression analysis, VIF values ranged from 1.