# statsmodels.robust.scale.hubers_scale¶

statsmodels.robust.scale.hubers_scale = <statsmodels.robust.scale.HuberScale object>

Huber’s scaling for fitting robust linear models.

Huber’s scale is intended to be used as the scale estimate in the IRLS algorithm and is slightly different than the Huber class.

Parameters: d (float, optional) – d is the tuning constant for Huber’s scale. Default is 2.5 tol (float, optional) – The convergence tolerance maxiter (int, optiona) – The maximum number of iterations. The default is 30.
statsmodels.robust.scale.call()

Return’s Huber’s scale computed as below

Notes

Huber’s scale is the iterative solution to

scale_(i+1)**2 = 1/(n*h)*sum(chi(r/sigma_i)*sigma_i**2

where the Huber function is

chi(x) = (x**2)/2 for |x| < d chi(x) = (d**2)/2 for |x| >= d

and the Huber constant h = (n-p)/n*(d**2 + (1-d**2)* scipy.stats.norm.cdf(d) - .5 - d*sqrt(2*pi)*exp(-0.5*d**2)