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# scikits.statsmodels.robust.scale.Huber

class `scikits.statsmodels.robust.scale.``Huber`(c=1.5, tol=1e-008, maxiter=30, norm=None)

  Huber’s proposal 2 for estimating location and scale jointly.

  Parameters: | c : float, optional Threshold used in threshold for chi=psi**2. Default value is 1.5. tol : float, optional Tolerance for convergence. Default value is 1e-08. maxiter : int, optional0 Maximum number of iterations. Default value is 30. norm : scikits.statsmodels.robust.norms.RobustNorm, optional A robust norm used in M estimator of location. If None, the location estimator defaults to a one-step fixed point version of the M-estimator using Huber’s T. call : Return joint estimates of Huber’s scale and location.

  Examples

      >>> import numpy as np
      >>> import scikits.statsmodels as sm
      >>> chem_data = np.array([2.20, 2.20, 2.4, 2.4, 2.5, 2.7, 2.8, 2.9, 3.03,
      ...        3.03, 3.10, 3.37, 3.4, 3.4, 3.4, 3.5, 3.6, 3.7, 3.7, 3.7, 3.7,
      ...        3.77, 5.28, 28.95])
      >>> sm.robust.scale.huber(chem_data)
      (array(3.2054980819923693), array(0.67365260010478967))

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