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We also discuss the performance of the proposed estimators compare to the alternative ridge regression method due to Liu (1993).
Using the ridge regression method, leave-one-out cross-validation scheme demonstrates recognition accuracies of 75.5% for the extraversion trait and 68.6% for the leadership trait.
The FEAR method is compared with Multiple Linear Regression (MLR) methods, as forward selection and stepwise regression, and with the alternative ridge regression method.
(C ) A 2D nonlinearity was estimated by ridge regression method from the samples marked in (B ).
MatLab code for the Cox regression based ridge regression method is provided by Bøvelstad et al. [ 36].
We hypothesized a nonlinear function f that takes inputs from the OSN spike rate y and its rate of change dy/ dt and whose output corresponds to the PN spike rate z; for example, z = f (y, d y d t ), and f was estimated using a 2D ridge regression method on 100 × 100 grids (Kim et al., 2011).
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It was found that the FEAR method performs better than the earlier applied MLR and ridge regression methods, since the significant main effects are more accurately estimated and because fewer effects are incorrectly considered being either significant or non-significant.
Elastic net regularization [ 71] is a technique that conbines the L1 and L2 penalties of the lasso and ridge regression methods.
Parameters for both lasso and ridge regression methods were obtained by minimizing the mean squared error function using nested 3-fold cross-validation on the training dataset.
> We compare the proposed method against its three close counterparts including multivariate linear regression (LR) method, ridge regression (RR) method and least absolute shrinkage and selection operator (Lasso) (Tibshirani, 1996) method.
For each of the five trials, within the training data, an internal 5-fold cross-validation is performed to fine tune the parameters in the range of for both ridge regression and our method.
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