nortsTest (version 1.1.2)

elbouch.test: Computes El Bouch, et al.'s test for normality of multivariate dependent samples.

Description

Computes the El Bouch, Michel, & Comon's test for normality of a bivariate dependent samples.

Usage

elbouch.test(y, x = NULL)

Value

A list with class "h.test" containing the following components:

statistic:

the El Bouch Z statistic.

p.value:

the p value for the test.

alternative:

a character string describing the alternative hypothesis.

method:

a character string “El Bouch, Michel & Comon's test”.

data.name:

a character string giving the name of the data.

Arguments

y

a numeric vector or an object of the ts class containing a stationary time series.

x

a numeric vector or an object of the ts class containing a stationary time series.

Author

Asael Alonzo Matamoros.

Details

This function computes El Bouch, et al. (2022) test for normality of bivariate dependent samples. If `x` is set to `NULL`, the test computes the univariate counterpart. This test is a correction of Mardia's, (1970) multivariate skewness and kurtosis test for multivariate samples.

References

El Bouch, S., Michel, O. & Comon, P. (2022). A normality test for Multivariate dependent samples. Journal of Signal Processing. Volume 201.

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

Lobato, I., & Velasco, C. (2004). A simple test of normality in time series. Journal of econometric theory. 20(4), 671-689.

See Also

lobato.test

Examples

# Generate an univariate stationary arma process
y = arima.sim(100,model = list(ar = 0.3))
elbouch.test(y)

# Generate a bivariate Gaussian random vector
x = rnorm(200)
y = rnorm(200)
elbouch.test(y = y, x = x)