A Two-Sample Test for Equality of Means in High Dimension.

ABSTRACT: We develop a test statistic for testing the equality of two population mean vectors in the "large-p-small-n" setting. Such a test must surmount the rank-deficiency of the sample covariance matrix, which breaks down the classic Hotelling T² test. The proposed procedure, called the generalized component test, avoids full estimation of the covariance matrix by assuming that the p components admit a logical ordering such that the dependence between components is related to their displacement. The test is shown to be competitive with other recently developed methods under ARMA and long-range dependence structures and to achieve superior power for heavy-tailed data. The test does not assume equality of covariance matrices between the two populations, is robust to heteros

SUBMITTER: Gregory KB 

PROVIDER: S-EPMC4533933 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature