Estimation When Both Covariance and Precision Matrices are Sparse

MacNamara, S; Schlogl, E; Botev, Z

Estimation When Both Covariance and Precision Matrices are Sparse

MacNamara, S Schlogl, E

Botev, Z

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings - Winter Simulation Conference, 2021, 2021-December, pp. 1-11
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	MacNamara, S
dc.contributor.author	Schlogl, E https://orcid.org/0000-0001-8898-6879
dc.contributor.author	Botev, Z
dc.date	2021-12-12
dc.date.accessioned	2022-06-08T21:23:31Z
dc.date.available	2022-06-08T21:23:31Z
dc.date.issued	2021-01-01
dc.identifier.citation	Proceedings - Winter Simulation Conference, 2021, 2021-December, pp. 1-11
dc.identifier.isbn	9781665433112
dc.identifier.issn	0891-7736
dc.identifier.uri	http://hdl.handle.net/10453/158016
dc.description.abstract	We offer a method to estimate a covariance matrix in the special case that both the covariance matrix and the precision matrix are sparse-a constraint we call double sparsity. The estimation method is maximum likelihood, subject to the double sparsity constraint. In our method, only a particular class of sparsity pattern is allowed: both the matrix and its inverse must be subordinate to the same chordal graph. Compared to a naive enforcement of double sparsity, our chordal graph approach exploits a special algebraic local inverse formula. This local inverse property makes computations that would usually involve an inverse (of either precision matrix or covariance matrix) much faster. In the context of estimation of covariance matrices, our proposal appears to be the first to find such special pairs of covariance and precision matrices.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings - Winter Simulation Conference
dc.relation.ispartof	2021 Winter Simulation Conference (WSC)
dc.relation.isbasedon	10.1109/WSC52266.2021.9715487
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Estimation When Both Covariance and Precision Matrices are Sparse
dc.type	Conference Proceeding
utslib.citation.volume	2021-December
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Strength - QFRC - Quantitative Finance Research Centre
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2024-02-23T00:00:00+1000Z
dc.date.updated	2022-06-08T21:23:29Z
pubs.finish-date	2021-12-15
pubs.publication-status	Published
pubs.start-date	2021-12-12
pubs.volume	2021-December

Abstract:

We offer a method to estimate a covariance matrix in the special case that both the covariance matrix and the precision matrix are sparse-a constraint we call double sparsity. The estimation method is maximum likelihood, subject to the double sparsity constraint. In our method, only a particular class of sparsity pattern is allowed: both the matrix and its inverse must be subordinate to the same chordal graph. Compared to a naive enforcement of double sparsity, our chordal graph approach exploits a special algebraic local inverse formula. This local inverse property makes computations that would usually involve an inverse (of either precision matrix or covariance matrix) much faster. In the context of estimation of covariance matrices, our proposal appears to be the first to find such special pairs of covariance and precision matrices.

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http://hdl.handle.net/10453/158016