Joint estimation of multiple high-dimensional precision matrices

dc.contributor.author

Cai, TT

dc.contributor.author

Li, H

dc.contributor.author

Liu, W

dc.contributor.author

Xie, J

dc.date.accessioned

2015-11-05T02:25:45Z

dc.date.issued

2016-04-01

dc.description.abstract

Motivated by analysis of gene expression data measured in different tissues or disease states, we consider joint estimation of multiple precision matrices to effectively utilize the partially shared graphical structures of the corresponding graphs. The procedure is based on a weighted constrained l∞/l1 minimization, which can be effectively implemented by a second-order cone programming. Compared to separate estimation methods, the proposed joint estimation method leads to estimators converging to the true precision matrices faster. Under certain regularity conditions, the proposed procedure leads to an exact graph structure recovery with a probability tending to 1. Simulation studies show that the proposed joint estimation methods outperform other methods in graph structure recovery. The method is illustrated through an analysis of an ovarian cancer gene expression data. The results indicate that the patients with poor prognostic subtype lack some important links among the genes in the apoptosis pathway.

dc.identifier.issn

1017-0405

dc.identifier.uri

https://hdl.handle.net/10161/10826

dc.publisher

Institute of Statistical Science

dc.relation.ispartof

Statistica Sinica

dc.relation.isversionof

10.5705/ss.2014.256

dc.title

Joint estimation of multiple high-dimensional precision matrices

dc.type

Journal article

duke.contributor.orcid

Xie, J|0000-0001-5905-6728

pubs.begin-page

445

pubs.end-page

464

pubs.issue

2

pubs.organisational-group

Basic Science Departments

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Biostatistics & Bioinformatics

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Duke

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School of Medicine

pubs.publication-status

Published

pubs.volume

26

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