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...://www.graphpad.com/articles/RobustNonlinearRegression_files/frame.htm> in short: instead of using the premise that the residuals are gaussian they propose a Lorentzian distribution, in stead of minimizing the squared residus SUM (Y-Yhat)^2, the objective function is now SUM log(1+(Y-Yhat)^2/ RobustSD) where RobustSD is the 68th percentile of the absolute value of the residues my question is: is there a smart and elegant way to change to objective function from squared Distance to log(1+D^2/Rsd^2) ? or alternatively to write this as a weighted non-linear regression where the weights are re...