2021 journal article
Bayesian estimation of sparse precision matrices in the presence of Gaussian measurement error
ELECTRONIC JOURNAL OF STATISTICS, 15(2), 4545–4579.
author keywords: High-dimensional inference; Gaussian graphical model; measurement error; posterior contraction rate; sparsity
TL;DR:
This paper incorporates measurement error in the context of estimating a sparse, high-dimensional precision matrix for a Gaussian graphical model with data corrupted by Gaussian measurement error with unknown variance and establishes a general result which gives sufficient conditions under which the posterior contraction rates that hold in the no-measurement-error case carry over to the measurement- error case.
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Added: February 28, 2022
Estimation of sparse, high-dimensional precision matrices is an important and challenging problem. Existing methods all assume that observations can be made precisely but, in practice, this often is not the case; for example, the instruments used to measure the response may have limited precision. The present paper incorporates measurement error in the context of estimating a sparse, high-dimensional precision matrix. In particular, for a Gaussian graphical model with data corrupted by Gaussian measurement error with unknown variance, we establish a general result which gives sufficient conditions under which the posterior contraction rates that hold in the no-measurement-error case carry over to the measurement-error case. Interestingly, this result does not require that the measurement error variance be small. We apply our general result to several cases with well-known prior distributions for sparse precision matrices and also to a case with a newly-constructed prior for precision matrices with a sparse factor-loading form. Two different simulation studies highlight the empirical benefits of accounting for the measurement error as opposed to ignoring it, even when that measurement error is relatively small.