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

In nortsTest: Assessing Normality of Stationary Process

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)

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.

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.

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.

Author(s)

Asael Alonzo Matamoros.

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)

nortsTest documentation built on May 29, 2024, 10:05 a.m.