Description Usage Arguments Details Value Author(s) References
Testing the equality of two sample high dimensional mean vectors using the method developed in arXiv:1406.1939 [math.ST]
1 2 |
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. |
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 of testing statistics, p-values (the non-studentized statistic and the studentized statistic respectively), alternative hypothesis, and the name of testing procedure.
Tong He
J. Chang, W. Zhou and W.-X. Zhou, Simulation-Based Hypothesis Testing of High Dimensional Means Under Covariance Heterogeneity (2014), arXiv:1406.1939.
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