Selection of Variables in Linear Regression Using the Minimum Sum of Weighted Absolute Errors Criterion

Abstract

An efficient implicit enumeration algorithm is proposed for the problem of selecting subsets of predictor variables in a multiple linear regression model using the minimum sum of weighted absolute errors (MSWAE) criterion. The proposed algorithm is illustrated with an example. Computational experience shows that the proposed algorithm is superior to the currently available algorithm in terms of computation time and the number of iterations required to solve a problem.