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CLC number: O212.8

On-line Access: 2013-01-31

Received: 2012-11-09

Revision Accepted: 2013-01-14

Crosschecked: 2013-01-14

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Journal of Zhejiang University SCIENCE C 2013 Vol.14 No.2 P.85-97

http://doi.org/10.1631/jzus.C1200316


Modeling correlated samples via sparse matrix Gaussian graphical models


Author(s):  Yi-zhou He, Xi Chen, Hao Wang

Affiliation(s):  Lancaster University Management School, Lancaster University, Lancaster, LA1 4YU, UK; more

Corresponding email(s):   y.he2@lancs.ac.uk, x.chen14@lancs.ac.uk, haowang@sc.edu

Key Words:  Gaussian graphical models, Hyper-inverse Wishart distributions, Mutual fund evaluation, Network


Yi-zhou He, Xi Chen, Hao Wang. Modeling correlated samples via sparse matrix Gaussian graphical models[J]. Journal of Zhejiang University Science C, 2013, 14(2): 85-97.

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author="Yi-zhou He, Xi Chen, Hao Wang",
journal="Journal of Zhejiang University Science C",
volume="14",
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pages="85-97",
year="2013",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1200316"
}

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%T Modeling correlated samples via sparse matrix Gaussian graphical models
%A Yi-zhou He
%A Xi Chen
%A Hao Wang
%J Journal of Zhejiang University SCIENCE C
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%P 85-97
%@ 1869-1951
%D 2013
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1200316

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T1 - Modeling correlated samples via sparse matrix Gaussian graphical models
A1 - Yi-zhou He
A1 - Xi Chen
A1 - Hao Wang
J0 - Journal of Zhejiang University Science C
VL - 14
IS - 2
SP - 85
EP - 97
%@ 1869-1951
Y1 - 2013
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1200316


Abstract: 
A new procedure of learning in gaussian graphical models is proposed under the assumption that samples are possibly dependent. This assumption, which is pragmatically applied in various areas of multivariate analysis ranging from bioinformatics to finance, makes standard gaussian graphical models (GGMs) unsuitable. We demonstrate that the advantage of modeling dependence among samples is that the true discovery rate and positive predictive value are improved substantially than if standard GGMs are applied and the dependence among samples is ignored. The new method, called matrix-variate gaussian graphical models (MGGMs), involves simultaneously modeling variable and sample dependencies with the matrix-normal distribution. The computation is carried out using a Markov chain Monte Carlo (MCMC) sampling scheme for graphical model determination and parameter estimation. Simulation studies and two real-world examples in biology and finance further illustrate the benefits of the new models.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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