Ridge parameters optimization based on minimizing model selection criterion in multivariate generalized ridge regression

Abstract

A multivariate generalized ridge (MGR) regression provides a shrinkage estimator of the multivariate linear regression by multiple ridge parameters. Since the ridge parameters which adjust the amount of shrinkage of the estimator are unknown, their optimization is an important task to obtain a better estimator. For the univariate case, a fast algorithm has been proposed for optimizing ridge parameters based on minimizing a model selection criterion (MSC) and the algorithm can be applied to various MSCs. In this paper, we extend this algorithm to MGR regression. We also describe the relationship between the MGR estimator which is not sparse and a multivariate adaptive group Lasso estimator which is sparse, under orthogonal explanatory variables.