The Gaussian Hare and the Laplacian Tortoise: Computability of squared-error vs. absolute-error estimators

Stephen Portnoy and Roger Koenker

Since Gauss(1821), it has been generally accepted that l_2 methods of combining observations by minimizing sums of squared errors have significant computational advantages over earlier l_1 methods based on minimization of absolute errors advocated by Boscovich, Laplace and others. However, l_1 methods are known to have significant robustness advantages over l_2 methods in many applications, and related quantile regression methods provide a useful, complementary approach to classical least-squares estimation of statistical models. Combining recent advances in interior point methods for solving linear programs with a new statistical preprocessing approach for l_1-type problems, we obtain a 10 to 100 fold improvement in computational speeds over current (simplex-based) l_1 algorithms in large problems, demonstrating that l_1 methods can be made competitive with l_2 methods in terms of computational speed throughout the entire range of problem sizes. Formal complexity results are given which suggest that l_1 regression can be made faster than least squares regression for n sufficiently large, and p modest.

Department of Statistics University of Illinois Urbana-Champaign

Contact: roger@ysidro.econ.uiuc.edu


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