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# scikits.statsmodels.robust.scale.HuberScale

class `scikits.statsmodels.robust.scale.``HuberScale`(d=2.5, tol=1e-008, maxiter=30)

  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

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