WIAS Preprint No. 2229, (2016)
Consistency results and confidence intervals for adaptive l1-penalized estimators of the high-dimensional sparse precision matrix
Authors
- Avanesov, Valeriy
- Polzehl, Jörg
ORCID: 0000-0001-7471-2658 - Tabelow, Karsten
ORCID: 0000-0003-1274-9951
2010 Mathematics Subject Classification
- 62J07 62P10
Keywords
- adaptive l1 penalty, precision matrix, high-dimensional statistics, sparsity, confidence intervals, functional connectivity
DOI
Abstract
In this paper we consider the adaptive l1-penalized estimators for the precision matrix in a finite-sample setting. We show consistency results and construct confidence intervals for the elements of the true precision matrix. Additionally, we analyze the bias of these confidence intervals. We apply the estimator to the estimation of functional connectivity networks in functional Magnetic Resonance data and elaborate the theoretical results in extensive simulation experiments.
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