biweight_location

astropy.stats.biweight_location(a, c=6.0, M=None, axis=None)[source] [edit on github]

Compute the biweight location.

The biweight location is a robust statistic for determining the central location of a distribution. It is given by:

\[\begin{split}C_{bl}= M+\frac{\Sigma_{\|u_i\|<1} (x_i-M)(1-u_i^2)^2} {\Sigma_{\|u_i\|<1} (1-u_i^2)^2}\end{split}\]

where \(M\) is the sample median (or the input initial guess) and \(u_i\) is given by:

\[u_{i} = \frac{(x_i-M)}{c\ MAD}\]

where \(c\) is the tuning constant and \(MAD\) is the median absolute deviation.

For more details, see Beers, Flynn, and Gebhardt (1990); AJ 100, 32.

Parameters:

a : array-like

Input array or object that can be converted to an array.

c : float, optional

Tuning constant for the biweight estimator. Default value is 6.0.

M : float or array-like, optional

Initial guess for the biweight location. An array can be input when using the axis keyword.

axis : int, optional

Axis along which the biweight locations are computed. The default (None) is to compute the biweight location of the flattened array.

Returns:

biweight_location : float or ndarray

The biweight location of the input data. If axis is None then a scalar will be returned, otherwise a ndarray will be returned.

Examples

Generate random variates from a Gaussian distribution and return the biweight location of the distribution:

>>> import numpy as np
>>> from astropy.stats import biweight_location
>>> rand = np.random.RandomState(12345)
>>> from numpy.random import randn
>>> loc = biweight_location(rand.randn(1000))
>>> print(loc)    
-0.0175741540445