Permutation-based multivariate regression analysis: The case for least sum of absolute deviations regression

Linear and nonlinear multivariate least sum of absolute deviations regression models are profiled and evaluated. A chance-corrected measure of agreement between observed and predicted values is presented, a technique for establishing empirically-derived quantile limits for predicted values is introduced, and a permutation-based inference procedure for the measure of agreement is described.

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