Regularization Parameter Selections via Generalized Information Criterion
Regularization Parameter Selections via Generalized Information Criterion
Abstract We apply the nonconcave penalized likelihood approach to obtain variable selections as well as shrinkage estimators. This approach relies heavily on the choice of regularization parameter, which controls the model complexity. In this paper, we propose employing the generalized information criterion, encompassing the commonly used Akaike information criterion (AIC) …