test.CS: multivariate normality test of Cox and Small

In mnt: Affine Invariant Tests of Multivariate Normality

Description

Performs the test of multivariate normality of Cox and Small (1978).

Usage

test.CS(data, MC.rep = 1000, alpha = 0.05, Points = NULL)

Arguments

data	a n x d matrix of d dimensional data vectors.
MC.rep	number of repetitions for the Monte Carlo simulation of the critical value.
alpha	level of significance of the test.
Points	number of points to approximate the maximum functional on the unit sphere.

Details

The test statistic is T_{n,CS}=\max_{b\in\{x\in\mathbf{R}^d:\|x\|=1\}}η_n^2(b), where

η_n^2(b)=\frac{≤ft\|n^{-1}∑_{j=1}^nY_{n,j}(b^\top Y_{n,j})^2\right\|^2-≤ft(n^{-1}∑_{j=1}^n(b^\top Y_{n,j})^3\right)^2}{n^{-1}∑_{j=1}^n(b^\top Y_{n,j})^4-1-≤ft(n^{-1}∑_{j=1}^n(b^\top Y_{n,j})^3\right)^2}

. Here, Y_{n,j}=S_n^{-1/2}(X_j-\overline{X}_n), j=1,…,n, are the scaled residuals, \overline{X}_n is the sample mean and S_n is the sample covariance matrix of the random vectors X_1,…,X_n. To ensure that the computation works properly n ≥ d+1 is needed. If that is not the case the test returns an error. Note that the maximum functional has to be approximated by a discrete version, for details see Ebner (2012).

Value

a list containing the value of the test statistic, the approximated critical value and a test decision on the significance level alpha:

$Test

name of the test.

$Test.value

the value of the test statistic.

$cv

the approximated critical value.

$Decision

the comparison of the critical value and the value of the test statistic.

#'

References

Cox, D.R., Small, N.J.H. (1978), Testing multivariate normality, Biometrika, 65:263-272.

Ebner, B. (2012), Asymptotic theory for the test for multivariate normality by Cox and Small, Journal of Multivariate Analysis, 111:368-379.

See Also

CS

Examples

test.CS(MASS::mvrnorm(10,c(0,1),diag(1,2)),MC.rep=100)

mnt documentation built on July 31, 2020, 5:07 p.m.