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Graphical model selection for Gaussian conditional random fields in the presence of latent variables

Abstract:

We consider the problem of learning a conditional Gaussian graphical model in the presence of latent variables. Building on recent advances in this field, we suggest a method that decomposes the parameters of a conditional Markov random field into the sum of a sparse and a low-rank matrix. We derive convergence bounds for this estimator and show that it is well-behaved in the high-dimensional regime as well as “sparsistent” (i.e., capable of recovering the graph structure). We then show how p...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

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Publisher copy:
10.1080/01621459.2018.1434531

Authors


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Institution:
University of Oxford
Division:
Medical Sciences
Department:
Kennedy Institute of Rheumatology
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Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Big Data Institute
Oxford college:
Linacre College
ORCID:
0000-0002-5012-4162
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Funding agency for:
McVean, G
University of Bristol More from this funder
Publisher:
Taylor & Francis Publisher's website
Journal:
Journal of the American Statistical Association Journal website
Pages:
1-12
Publication date:
2018-07-11
Acceptance date:
2018-02-13
DOI:
EISSN:
1537-274X
ISSN:
0162-1459
Pubs id:
pubs:906074
URN:
uri:539e3785-8309-43a5-a360-cc73b04b9907
UUID:
uuid:539e3785-8309-43a5-a360-cc73b04b9907
Local pid:
pubs:906074

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