stats.stattools.robust_kurtosis()

statsmodels.stats.stattools.robust_kurtosis

statsmodels.stats.stattools.robust_kurtosis(y, axis=0, ab=(5.0, 50.0), dg=(2.5, 25.0), excess=True) [source]

Calculates the four kurtosis measures in Kim & White

Parameters:

y : array-like axis : int or None, optional Axis along which the kurtoses are computed. If None, the entire array is used. ab: iterable, optional Contains 100*(alpha, beta) in the kr3 measure where alpha is the tail quantile cut-off for measuring the extreme tail and beta is the central quantile cutoff for the standardization of the measure db: iterable, optional Contains 100*(delta, gamma) in the kr4 measure where delta is the tail quantile for measuring extreme values and gamma is the central quantile used in the the standardization of the measure excess : bool, optional If true (default), computed values are excess of those for a standard normal distribution.

Returns:

kr1 : ndarray The standard kurtosis estimator. kr2 : ndarray Kurtosis estimator based on octiles. kr3 : ndarray Kurtosis estimators based on exceedence expectations. kr4 : ndarray Kurtosis measure based on the spread between high and low quantiles.

Notes

The robust kurtosis measures are defined

where is the estimated quantile at .

[R58]

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