A special purpose linear programming algorithm for obtaining least absolute value estimators in a linear model with dummy variables

Abstract

Dummy (0, 1) variables are frequently used in statistical modeling to represent the effect of certain extraneous factors. This paper presents a special purpose linear programming algorithm for obtaining least-absolute-value estimators in a linear model with dummy variables. The algorithm employs a compact basis inverse procedure and incorporates the advanced basis exchange techniques available in specialized algorithms for the general linear least-absolute-value problem. Computational results with a computer code version of the algorithm are given.

Keywords:

• L₁ norm
• analysis of covariance
• regression
• generalized upper bounding