Highly efficient hypothesis testing methods for regression-type tests with correlated observations and heterogeneous variance structure.

ABSTRACT:

Background

For many practical hypothesis testing (H-T) applications, the data are correlated and/or with heterogeneous variance structure. The regression t-test for weighted linear mixed-effects regression (LMER) is a legitimate choice because it accounts for complex covariance structure; however, high computational costs and occasional convergence issues make it impractical for analyzing high-throughput data. In this paper, we propose computationally efficient parametric and semiparametric tests based on a set of specialized matrix techniques dubbed as the PB-transformation. The PB-transformation has two advantages: 1. The PB-transformed data will have a scalar variance-covariance matrix. 2. The original H-T problem will be reduced to an equivalent one-sample H-T problem. The tran

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC6466736 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature