Penalized estimation of the Gaussian graphical model from data with replicates.

ABSTRACT: Gaussian graphical models are usually estimated from unreplicated data. The data are, however, likely to comprise signal and noise. These two cannot be deconvoluted from unreplicated data. Pragmatically, the noise is then ignored in practice. We point out the consequences of this practice for the reconstruction of the conditional independence graph of the signal. Replicated data allow for the deconvolution of signal and noise and the reconstruction of former's conditional independence graph. Hereto we present a penalized Expectation-Maximization algorithm. The penalty parameter is chosen to maximize the F-fold cross-validated log-likelihood. Sampling schemes of the folds from replicated data are discussed. By simulation we investigate the effect of replicates on the reconstruction of the s

SUBMITTER: van Wieringen WN 

PROVIDER: S-EPMC8360145 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature