MAKurt {mnt} | R Documentation |
multivariate kurtosis in the sense of Malkovich and Afifi
Description
This function computes the invariant measure of multivariate sample kurtosis due to Malkovich and Afifi (1973).
Usage
MAKurt(data, Points = NULL)
Arguments
data |
a n x d matrix of d dimensional data vectors. |
Points |
points for approximation of the maximum on the sphere. |
Details
Multivariate sample skewness due to Malkovich and Afifi (1973) is defined by
b_{n,d,M}^{(1)}=\max_{u\in \{x\in\mathbf{R}^d:\|x\|=1\}}\frac{\left(\frac{1}{n}\sum_{j=1}^n(u^\top X_j-u^\top \overline{X}_n )^3\right)^2}{(u^\top S_n u)^3},
where \overline{X}_n
is the sample mean and S_n
is the sample covariance matrix of the random vectors X_1,\ldots,X_n
. To ensure that the computation works properly
n \ge d+1
is needed. If that is not the case the function returns an error.
Value
value of sample kurtosis in the sense of Malkovich and Afifi.
References
Malkovich, J.F., and Afifi, A.A. (1973), On tests for multivariate normality, J. Amer. Statist. Ass., 68:176–179.
Henze, N. (2002), Invariant tests for multivariate normality: a critical review, Statistical Papers, 43:467–506.
Examples
MAKurt(MASS::mvrnorm(50,c(0,1),diag(1,2)))