ts_sd2008 {HDNRA}

Test proposed by Srivastava and Du (2008)

Description

Srivastava and Du (2008)'s test for testing equality of two-sample high-dimensional mean vectors with assuming that two covariance matrices are the same.

Usage

ts_sd2008(y1, y2)

Arguments

y1

The data matrix (p by n1) from the first population. Each column represents a p-dimensional observation.

y2

The data matrix (p by n2) from the first population. Each column represents a p-dimensional observation.

Details

Suppose we have two independent high-dimensional samples:

\boldsymbol{y}_{i1},\ldots,\boldsymbol{y}_{in_i}, \;\operatorname{are \; i.i.d. \; with}\; \operatorname{E}(\boldsymbol{y}_{i1})=\boldsymbol{\mu}_i,\; \operatorname{Cov}(\boldsymbol{y}_{i1})=\boldsymbol{\Sigma},i=1,2.

The primary object is to test

H_{0}: \boldsymbol{\mu}_1 = \boldsymbol{\mu}_2\; \operatorname{versus}\; H_{1}: \boldsymbol{\mu}_1 \neq \boldsymbol{\mu}_2.

Srivastava and Du (2008) proposed the following test statistic:

T_{SD} = \frac{n^{-1}n_1n_2(\bar{\boldsymbol{y}}_1 - \bar{\boldsymbol{y}}_2)^\top \boldsymbol{D}_S^{-1}(\bar{\boldsymbol{y}}_1 - \bar{\boldsymbol{y}}_2) - \frac{(n-2)p}{n-4}}{\sqrt{2 \left[\operatorname{tr}(\boldsymbol{R}^2) - \frac{p^2}{n-2}\right] c_{p, n}}},

where \bar{\boldsymbol{y}}_{i},i=1,2 are the sample mean vectors, \boldsymbol{D}_S is the diagonal matrix of sample variance, \boldsymbol{R} is the sample correlation matrix and c_{p, n} is the adjustment coefficient proposed by Srivastava and Du (2008). They showed that under the null hypothesis, T_{SD} is asymptotically normally distributed.

Value

A (list) object of S3 class htest containing the following elements:

statistic

the test statistic proposed by Srivastava and Du (2008).

p.value

the p-value of the test proposed by Srivastava and Du (2008).

cpn

the adjustment coefficient proposed by Srivastava and Du (2008).

References

Srivastava MS, Du M (2008). “A test for the mean vector with fewer observations than the dimension.” Journal of Multivariate Analysis, 99(3), 386–402. doi:10.1016/j.jmva.2006.11.002.

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_sd2008(y1, y2)