MVNMIX: Random Generation for the Normal Mixture Distribution

In mvnormalTest: Powerful Tests for Multivariate Normality

Random Generation for the Normal Mixture Distribution

Description

Generate univariate or multivariate random sample for the normal mixture distribution with density \lambda N(0,\sum_1)+(1-\lambda)N(bl, \sum_2), where l is the column vector with all elements being 1, \sum_i=(1-\rho_i)I+\rho_ill^T for i=1,2. \rho has to satisfy \rho > -1/(p-1) in order to make the covariance matrix meaningful.

Usage

MVNMIX(n, p, lambda, mu2, rho1 = 0, rho2 = 0)

Arguments

n	number of rows (observations).
p	total number of columns (variables).
lambda	weight parameter to allocate the proportions of the mixture, 0<\lambda<1.
mu2	is bl of N(bl, \sum_2).
rho1	parameter in \sum_1.
rho2	parameter in \sum_2.

Value

Returns univariate (p=1) or multivariate (p>1) random sample matrix.

References

Zhou, M., & Shao, Y. (2014). A powerful test for multivariate normality. Journal of applied statistics, 41(2), 351-363.

Examples

set.seed(12345)

## Generate 5X2 random sample matrix from MVNMIX(0.5,4,0,0) ##
MVNMIX(n=5, p=2, lambda=0.5, mu2=4, rho1=0, rho2=0)


## Power calculation against bivariate (p=2) MVNMIX(0.5,4,0,0) distribution ##
## at sample size n=50 at one-sided alpha = 0.05 ##

# Zhou-Shao's test #
power.mvnTest(a=0.05, n=50, p=2, B=100, FUN=MVNMIX, lambda=0.5, mu2=4, rho1=0, rho2=0)

mvnormalTest documentation built on June 8, 2025, 2 p.m.