stats.stattools.robust_skewness()

statsmodels.stats.stattools.robust_skewness

statsmodels.stats.stattools.robust_skewness(y, axis=0) [source]

Calculates the four skewness measures in Kim & White

Parameters:

Parameters:	y : array-like axis : int or None, optional Axis along which the skewness measures are computed. If `None`, the entire array is used.
Returns:	sk1 : ndarray The standard skewness estimator. sk2 : ndarray Skewness estimator based on quartiles. sk3 : ndarray Skewness estimator based on mean-median difference, standardized by absolute deviation. sk4 : ndarray Skewness estimator based on mean-median difference, standardized by standard deviation.

y : array-like

axis : int or None, optional

Axis along which the skewness measures are computed. If None, the entire array is used.

Returns:

sk1 : ndarray

The standard skewness estimator.

sk2 : ndarray

Skewness estimator based on quartiles.

sk3 : ndarray

Skewness estimator based on mean-median difference, standardized by absolute deviation.

sk4 : ndarray

Skewness estimator based on mean-median difference, standardized by standard deviation.

The robust skewness measures are defined

$SK_{2}=\frac{\left(q_{.75}-q_{.5}\right) -\left(q_{.5}-q_{.25}\right)}{q_{.75}-q_{.25}}$

$SK_{3}=\frac{\mu-\hat{q}_{0.5}} {\hat{E}\left[\left|y-\hat{\mu}\right|\right]}$

$SK_{4}=\frac{\mu-\hat{q}_{0.5}}{\hat{\sigma}}$

[R59]

Tae-Hwan Kim and Halbert White, “On more robust estimation of skewness and kurtosis,” Finance Research Letters, vol. 1, pp. 56-73, March 2004.

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© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.stats.stattools.robust_skewness.html