3.10.3.3.2. statsmodels.robust.scale.HuberScale¶
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class
statsmodels.robust.scale.HuberScale(d=2.5, tol=1e-08, maxiter=30)[source]¶ 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.
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)
Methods
call Return’s Huber’s scale computed as below