Abstract
In the context of Gaussian Graphical Models (GGMs) with high- dimensional small sample data, we present a simple procedure to esti- mate partial correlations under the constraint that some of them are strictly zero. This method can also be extended to covariance selection. If the goal is to estimate a GGM, our new procedure can be applied to re-estimate the partial correlations after a first graph has been esti- mated in the hope to improve the estimation of non-zero coefficients. In a simulation study, we compare our new covariance selection procedure to existing methods and show that the re-estimated partial correlation coefficients may be closer to the real values in important cases.
Item Type: | Paper |
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Form of publication: | Preprint |
Keywords: | Gaussian Graphical Models, high dimensional data, partial correlation estimation, covariance selection. |
Faculties: | Mathematics, Computer Science and Statistics > Statistics > Technical Reports |
Subjects: | 500 Science > 500 Science |
URN: | urn:nbn:de:bvb:19-epub-14280-9 |
Language: | English |
Item ID: | 14280 |
Date Deposited: | 04. Dec 2012 18:22 |
Last Modified: | 04. Nov 2020 12:54 |