Description Usage Arguments Details Value References Examples
Compute Khattree-Bahuguna's Univariate Skewness.
1 | kbSkew(x)
|
x |
a vector of original observations. |
Given a univariate random sample of size n consist of observations x_1, x_2, …, x_n, let x_{(1)} ≤ x_{(2)} ≤ \cdots ≤ x_{(n)} be the order statistics of x_1, x_2, …, x_n after being centered by their mean. Define
y_ i = \frac{x_{(i)} + x_{(n - i + 1)}}{2}
and
w_ i = \frac{x_{(i)} - x_{(n - i + 1)}}{2}
The sample Khattree-Bahuguna's univariate skewness is defined as
\hat{δ} = \frac{∑ y_i^2}{∑ y_i^2 + ∑ w_i^2}.
It can be shown that 0 ≤ \hat{δ} ≤ \frac{1}{2}. Values close to zero indicate, low skewness while those close to \frac{1}{2} indicate the presence of high degree of skewness.
kbSkew
gives the Khattree-Bahuguna's univariate skewness of the data.
Khattree, R. and Bahuguna, M. (2019). An alternative data analytic approach to measure the univariate and multivariate skewness. International Journal of Data Science and Analytics, Vol. 7, No. 1, 1-16.
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