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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 guarantees are established for parameter estimation and for this test. We show that our proposal is able to estimate latent variable graphical models more accurately than some existing proposals, and apply the proposed method to a brain imaging dataset.

SUBMITTER: Tan KM

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

REPOSITORIES: biostudies-literature

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

Tan Kean Ming KM Ning Yang Y Witten Daniela M DM Liu Han H

Biometrika 20161208 4

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 re ...[more]

PMID: 28736452

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Dataset Information

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

Publications

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

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