oneMean: CZZZ-test for one sample mean vector

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

Description

Testing the equality of high dimensional mean vector to zero using the method developed in arXiv:1406.1939 [math.ST]

Usage

oneMean(X, m = 2500, filter = TRUE, S = NULL, alpha = 0.05, DNAME)

Arguments

X	The n x p data matrix.
m	The number of Monte-Carlo samples in the test, default to be 2500
filter	A logical indicator of the filtering process, defaul to be TRUE
S	Covariance matrix of X, if not presented it will be estimated from the input sample.
alpha	The significant level of the test.
DNAME	Defaul 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=μ_0 for some prescribed value μ_0 which can be subtracted from the data. 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.