yatsm.regression.robust_fit module¶
Perform an iteratively re-weighted least squares ‘robust regression’. Basically a clone of statsmodels.robust.robust_linear_model.RLM without all the lovely, but costly, creature comforts.
- Reference:
- http://statsmodels.sourceforge.net/stable/rlm.html http://cran.r-project.org/web/packages/robustreg/index.html http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-robust-regression.pdf
Run this file to test performance gains. Implementation is ~3x faster than statsmodels and can reach ~4x faster if Numba is available to accelerate.
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yatsm.regression.robust_fit.
bisquare
(resid, c=4.685)[source]¶ Returns weighting for each residual using bisquare weight function
Parameters: - resid (np.ndarray) – residuals to be weighted
- c (float) – tuning constant for Tukey’s Biweight (default: 4.685)
Returns: weights for residuals
Return type: weight (ndarray)
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yatsm.regression.robust_fit.
mad
(resid, c=0.6745)[source]¶ Returns Median-Absolute-Deviation (MAD) for residuals
Parameters: - resid (np.ndarray) – residuals
- c (float) – scale factor to get to ~standard normal (default: 0.6745) (i.e. 1 / 0.75iCDF ~= 1.4826 = 1 / 0.6745)
Returns: MAD ‘robust’ variance estimate
Return type: