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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