# statsmodels.stats.stattools.robust_skewness¶

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

Calculates the four skewness measures in Kim & White

Parameters: y (array-like) – axis (int or None, optional) – Axis along which the skewness measures are computed. If None, the entire array is used. 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.

Notes

The robust skewness measures are defined

$SK_{2}=\frac{\left(q_{.75}-q_{.5}\right) -\left(q_{.5}-q_{.25}\right)}{q_{.75}-q_{.25}}$
$SK_{3}=\frac{\mu-\hat{q}_{0.5}} {\hat{E}\left[\left|y-\hat{\mu}\right|\right]}$
$SK_{4}=\frac{\mu-\hat{q}_{0.5}}{\hat{\sigma}}$
 [*] Tae-Hwan Kim and Halbert White, “On more robust estimation of skewness and kurtosis,” Finance Research Letters, vol. 1, pp. 56-73, March 2004.