Simulation-based hypothesis testing of high dimensional means under covariance heterogeneity

Chang, Jinyuan and Zheng, Chao and Zhou, Wen-Xin and Zhou, Wen (2017) Simulation-based hypothesis testing of high dimensional means under covariance heterogeneity. Biometrics, 73 (4). pp. 1300-1310. ISSN 0006-341X

Full text not available from this repository.

Abstract

In this article, we study the problem of testing the mean vectors of high dimensional data in both one-sample and two-sample cases. The proposed testing procedures employ maximum-type statistics and the parametric bootstrap techniques to compute the critical values. Different from the existing tests that heavily rely on the structural conditions on the unknown covariance matrices, the proposed tests allow general covariance structures of the data and therefore enjoy wide scope of applicability in practice. To enhance powers of the tests against sparse alternatives, we further propose two-step procedures with a preliminary feature screening step. Theoretical properties of the proposed tests are investigated. Through extensive numerical experiments on synthetic data sets and an human acute lymphoblastic leukemia gene expression data set, we illustrate the performance of the new tests and how they may provide assistance on detecting disease-associated gene-sets. The proposed methods have been implemented in an R-package HDtest and are available on CRAN.

Item Type:
Journal Article
Journal or Publication Title:
Biometrics
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2700
Subjects:
ID Code:
87207
Deposited By:
Deposited On:
31 Jul 2017 12:52
Refereed?:
Yes
Published?:
Published
Last Modified:
20 Oct 2020 05:14