twoMeans: CZZZ-test for two sample mean vectors

In HDtest: High Dimensional Hypothesis Testing for Mean Vectors, Covariance Matrices, and White Noise of Vector Time Series

Description

Testing the equality of two sample high dimensional mean vectors using the method developed in arXiv:1406.1939 [math.ST]

Usage

twoMeans(X, Y, m = 2500, filter = TRUE, SX = NULL, SY = NULL,
  alpha = 0.05, DNAME)

Arguments

X	The n x p training data matrix.
Y	The n x p training data matrix.
m	The number of repetition in the test, default to be 2500
filter	A logical indicator of the filtering process, default to be TRUE
SX	The covariance matrix of X, if not presented it will be estimated from the input sample.
SY	The covariance matrix of T, if not presented it will be estimated from the input sample.
alpha	The significant level of the test.
DNAME	Defaulf input.

Details

Implement the method developed in arXiv:1406.1939 [math.ST] to test whether a high dimensional mean vector is zero or not, which is equivalent to test H_0: μ_1=μ_2. The procedure utilizes bootstrap concept and derive the critical values using independent Gaussian vectors whose covariance is estimated using sample covariance matrix.

Value

Value of testing statistics, p-values (the non-studentized statistic and the studentized statistic respectively), alternative hypothesis, and the name of testing procedure.

Author(s)

Tong He

References

J. Chang, W. Zhou and W.-X. Zhou, Simulation-Based Hypothesis Testing of High Dimensional Means Under Covariance Heterogeneity (2014), arXiv:1406.1939.

HDtest documentation built on May 2, 2019, 11:53 a.m.