Replicates in high dimensions, with applications to latent variable graphical models.

ABSTRACT: In classical statistics, much thought has been put into experimental design and data collection. In the high-dimensional setting, however, experimental design has been less of a focus. In this paper, we stress the importance of collecting multiple replicates for each subject in this setting. We consider learning the structure of a graphical model with latent variables, under the assumption that these variables take a constant value across replicates within each subject. By collecting multiple replicates for each subject, we are able to estimate the conditional dependence relationships among the observed variables given the latent variables. To test the null hypothesis of conditional independence between two observed variables, we propose a pairwise decorrelated score test. Theoretical guar

SUBMITTER: Tan KM 

PROVIDER: S-EPMC5520622 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature