Modeling correlated samples via sparse matrix Gaussian graphical models

Yi-zhou HE; Xi CHEN; Hao WANG

doi:10.1631/jzus.C1200316

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Modeling correlated samples via sparse matrix Gaussian graphical models

Computer Engineering | Updated：2022-12-09

- Modeling correlated samples via sparse matrix Gaussian graphical models
- Modeling correlated samples via sparse matrix Gaussian graphical models
- Frontiers of Information Technology & Electronic Engineering Vol. 14, Issue 2, Pages: 107-117(2013)
- Affiliations：
  
  1.Lancaster University Management School, Lancaster University, Lancaster, LA1 4YU, UK
  2.Department of Statistics, University of South Carolina, Columbia, South Carolina 29201, USA
- Author bio：
  
  [ "Yi-zhou HE, E-mail:y.he2@lancs.ac.uk" ]
  [ "Xi CHEN, E-mail:x.chen14@lancs.ac.uk" ]
  Hao WANG, E-mail: haowang@sc.edu
- Funds：
- DOI：10.1631/jzus.C1200316
  CLC： O212.8
- Received：09 November 2012，
  
  Revised：2013-;1-14，
  
  Published：2013-02
- Accepted：
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Yi-zhou HE, Xi CHEN, Hao WANG. Modeling correlated samples via sparse matrix Gaussian graphical models[J]. Frontiers of Information Technology & Electronic Engineering, 2013, 14(2): 107-117.
DOI：

Yi-zhou HE, Xi CHEN, Hao WANG. Modeling correlated samples via sparse matrix Gaussian graphical models[J]. Frontiers of Information Technology & Electronic Engineering, 2013, 14(2): 107-117. DOI： 10.1631/jzus.C1200316.

摘要

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.

关键词

Keywords

references

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