kurtosis: Mardia's multivariate skewness and kurtosis coefficients

In fastmatrix: Fast Computation of some Matrices Useful in Statistics

Mardia's multivariate skewness and kurtosis coefficients

Description

Functions to compute measures of multivariate skewness (b_{1p}) and kurtosis (b_{2p}) proposed by Mardia (1970),

b_{1p} = \frac{1}{n^2}\sum\limits_{i=1}^n\sum\limits_{j=1}^n ((\bold{x}_i - \overline{\bold{x}})^T\bold{S}^{-1}(\bold{x}_j - \overline{\bold{x}}))^3,

and

b_{2p} = \frac{1}{n}\sum\limits_{i=1}^n ((\bold{x}_i - \overline{\bold{x}})^T \bold{S}^{-1}(\bold{x}_j - \overline{\bold{x}}))^2.

Usage

kurtosis(x)

skewness(x)

Arguments

x	matrix of data with, say, p columns.

References

Mardia, K.V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika 57, 519-530.

Mardia, K.V., Zemroch, P.J. (1975). Algorithm AS 84: Measures of multivariate skewness and kurtosis. Applied Statistics 24, 262-265.

Examples

setosa <- iris[1:50,1:4]
kurtosis(setosa)
skewness(setosa)

fastmatrix documentation built on Oct. 12, 2023, 5:14 p.m.