Model selection in multi-response regression with grouped variables

Abstract

We propose the multi-response regression with grouped variables algorithm. This algorithm is an input selection method developed to solve the problem when there are more than one response variables and the input variables may correlated. This forward selection procedure is a nature extension of the grouped Least Angle Regression algorithm and the multi-response sparse regression algorithm. We provide three different variants of the algorithm regarding the rule of choosing the step length. The performance of our algorithm measured by prediction accuracy and performance of factor selection was studied based on experiments with simulated data and a real dataset. The proposed algorithm reveals an overall better performance compared with grouped Least Angle Regression algorithm when using the same experiments.