Source :
Computational Statistics & Data Analysis
Descriptors :
High dimensionality , Hypothesis test , Likelihood ratio , Normal mean , Union–intersection test
Abstract :
The problem of testing the population mean vector of high-dimensional multivariate data
is considered. Inspired by Roy’s union–intersection test, a generalized high-dimensional
likelihood ratio test for the normal population mean vector is proposed. The p-value for
the test is obtained by using randomization method, which does not rely on assumptions
about the structure of the covariance matrix. An interpretation of the new statistic is
given, which does not rely on the normality assumption. Hence the proposed test is also
available for non-normal multivariate population. Simulation studies show that the new
test offers higher power than other two competing tests when the variables are dependent
and performs particularly well for non-normal multivariate population
Title of Article :
A generalized likelihood ratio test for normal mean when p is
greater than n
Author/Authors :
Zhao, Junguang , Xu, Xingzhong
Author/Authors - جزئيات :
