statsmodels.robust.scale.Huber¶

class statsmodels.robust.scale.Huber(c=1.5, tol=1e-08, maxiter=30, norm=None)[source]¶

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

Parameters:

cfloat, optional: Threshold used in threshold for chi=psi**2. Default value is 1.5.
tolfloat, optional: Tolerance for convergence. Default value is 1e-08.
maxiterint, optional0: Maximum number of iterations. Default value is 30.
normstatsmodels.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 statsmodels.api 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))

Methods

__call__(a[, mu, initscale, axis])

Compute Huber's proposal 2 estimate of scale, using an optional initial value of scale and an optional estimate of mu.

Methods