High-Dimensional Precision Matrix Estimation through GSOS with Application in the Foreign Exchange Market
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Published:2022-11-12
Issue:22
Volume:10
Page:4232
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ISSN:2227-7390
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Container-title:Mathematics
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language:en
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Short-container-title:Mathematics
Author:
Kheyri AzamORCID,
Bekker AndrietteORCID,
Arashi MohammadORCID
Abstract
This article studies the estimation of the precision matrix of a high-dimensional Gaussian network. We investigate the graphical selector operator with shrinkage, GSOS for short, to maximize a penalized likelihood function where the elastic net-type penalty is considered as a combination of a norm-one penalty and a targeted Frobenius norm penalty. Numerical illustrations demonstrate that our proposed methodology is a competitive candidate for high-dimensional precision matrix estimation compared to some existing alternatives. We demonstrate the relevance and efficiency of GSOS using a foreign exchange markets dataset and estimate dependency networks for 32 different currencies from 2018 to 2021.
Funder
National Research Foundation
South African DST-NRF-MRC SARChI Research Chair in Biostatistics
Ferdowsi University of Mashhad
Subject
General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)
Reference25 articles.
1. Sparse inverse covariance estimation with the graphical lasso;Biostatistics,2008
2. Network exploration via the adaptive LASSO and SCAD penalties;Ann. Appl. Stat.,2009
3. Sparse estimation of a covariance matrix;Biometrika,2011
4. New insights and faster computations for the graphical lasso;J. Comput. Gr. Stat.,2011
5. The graphical lasso: New insights and alternatives;Electron. J. Stat.,2012