ts_zgzc2020 {HDNRA} | R Documentation |
Test proposed by Zhang et al. (2020)
Description
Zhang et al. (2020)'s test for testing equality of two-sample high-dimensional mean vectors with assuming that two covariance matrices are the same.
Usage
ts_zgzc2020(y1, y2)
Arguments
y1 |
The data matrix ( |
y2 |
The data matrix ( |
Details
Suppose we have two independent high-dimensional samples:
The primary object is to test
Zhang et al.(2020) proposed the following test statistic:
where are the sample mean vectors.
They showed that under the null hypothesis,
and a chi-squared-type mixture have the same normal or non-normal limiting distribution.
Value
A (list) object of S3
class htest
containing the following elements:
- p.value
the p-value of the test proposed by Zhang et al. (2020).
- statistic
the test statistic proposed by Zhang et al. (2020).
- beta
parameter used in Zhang et al. (2020)'s test.
- df
estimated approximate degrees of freedom of Zhang et al. (2020)'s test.
References
Zhang J, Guo J, Zhou B, Cheng M (2020). “A simple two-sample test in high dimensions based on L 2-norm.” Journal of the American Statistical Association, 115(530), 1011–1027. doi:10.1080/01621459.2019.1604366.
Examples
set.seed(1234)
n1 <- 20
n2 <- 30
p <- 50
mu1 <- t(t(rep(0, p)))
mu2 <- mu1
rho <- 0.1
y <- (-2 * sqrt(1 - rho) + sqrt(4 * (1 - rho) + 4 * p * rho)) / (2 * p)
x <- y + sqrt((1 - rho))
Gamma <- matrix(rep(y, p * p), nrow = p)
diag(Gamma) <- rep(x, p)
Z1 <- matrix(rnorm(n1 * p, mean = 0, sd = 1), p, n1)
Z2 <- matrix(rnorm(n2 * p, mean = 0, sd = 1), p, n2)
y1 <- Gamma %*% Z1 + mu1 %*% (rep(1, n1))
y2 <- Gamma %*% Z2 + mu2 %*% (rep(1, n2))
ts_zgzc2020(y1, y2)