Standard and robust orthogonal regression

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Abstract
A fast routine for converting regression algorithms into corresponding orthogonal regression (OR) algorithms was introduced in Ammann and Van Ness (1988). The present paper discusses the properties of various ordinary and robust OR procedures created using this routine. OR minimizes the sum of the orthogonal distances from the regression plane to the data points. OR has three types of applications. First, L 2 OR is the maximum likelihood solution of the Gaussian errors-in-variables (EV) regression problem. This L 2 solution is unstable, thus the robust OR algorithms created from robust regression algorithms should prove very useful. Secondly, OR is intimately related to principal components analysis. Therefore, the routine can also be used to create L 1, robust, etc. principal components algorithms. Thirdly, OR treats the x and y variables symmetrically which is important in many modeling problems. Using Monte Carlo studies this paper compares the performance of standard regression, robust regression, OR, and robust OR on Gaussian EV data, contaminated Gaussian EV data, heavy-tailed EV data, and contaminated heavy-tailed EV data.