Robust fit in Python: weighted and generalized nonlinear least squares -


i have heteroscedastic data set has fitted logistic function. in respect have few quesions.

  1. is there nonlinear generalised least squares (ls) implmentation in python?

  2. what advantages of using generalised ls on iteratively reweighted ls?

  3. an additional question regarding scipy.optiize.least_squares. there keyword f_scale, commonly set 0.1. how defined in general? understand f_scale gives range of inliers, how determine optimal range?


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