Please wait a minute...
FITEE
Front. Inform. Technol. Electron. Eng.  2013, Vol. 14 Issue (2): 107-117    DOI: 10.1631/jzus.C1200316
    
Modeling correlated samples via sparse matrix Gaussian graphical models
Yi-zhou He, Xi Chen, Hao Wang
Lancaster University Management School, Lancaster University, Lancaster, LA1 4YU, UK; Department of Statistics, University of South Carolina, Columbia, South Carolina 29201, USA
Modeling correlated samples via sparse matrix Gaussian graphical models
Yi-zhou He, Xi Chen, Hao Wang
Lancaster University Management School, Lancaster University, Lancaster, LA1 4YU, UK; Department of Statistics, University of South Carolina, Columbia, South Carolina 29201, USA
 全文: PDF 
摘要： 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： Gaussian graphical models;  Hyper-inverse Wishart distributions;  Mutual fund evaluation;  Network    
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.
Key words: Gaussian graphical models    Hyper-inverse Wishart distributions    Mutual fund evaluation    Network
收稿日期: 2012-11-09 出版日期: 2013-01-31
CLC:  O212.8  
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章　　
Yi-zhou He
Xi Chen
Hao Wang

引用本文:

Yi-zhou He, Xi Chen, Hao Wang. Modeling correlated samples via sparse matrix Gaussian graphical models. Front. Inform. Technol. Electron. Eng., 2013, 14(2): 107-117.

链接本文:

http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1200316    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2013/V14/I2/107

[1] Ling ZHOU, Zhi-zhong TAN , Qing-hua ZHANG. A fractional-order multifunctional n-step honeycomb RLC circuit network[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(8): 1186-1196.
[2] Shih-kung LAI, Jhong-you HUANG. Theoretical foundation of a decision network for urban development[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(8): 1033-1039.
[3] Da-hui GAO , Qing-feng WANG , Yong LEI. Distributed fault-tolerant strategy for electric swing system of  hybrid excavators under communication errors[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(7): 941-954.
[4] Zhi-yong FENG, Ze-bing FENG, T. Aaron GULLIVER. Joint user association and resource partition for downlink-uplink decoupling in multi-tier HetNets[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(6): 817-829.
[5] He-hao NIU, Bang-ning ZHANG, Dao-xing GUO, Yu-zhen HUANG, Ming-yue LU. Joint cooperative beamforming and artificial noise design for secure AF relay networks with energy-harvesting eavesdroppers[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(6): 850-862.
[6] Lei-ming ZHANG, Long-hao TANG, Yong LEI. Controller area network node reliability assessment based on observable node information*#[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(5): 615-626.
[7] Yong-ping DU, Chang-qing YAO , Shu-hua HUO, Jing-xuan LIU. A new item-based deep network structure using a restricted Boltzmann machine for collaborative filtering[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(5): 658-666.
[8] Yue-bin LUO, Bao-sheng WANG, Xiao-feng WANG, Bo-feng ZHANG. A keyed-hashing based self-synchronization mechanism for port address hopping communication[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(5): 719-728.
[9] Wen-yan CUI, Xiang-ru MENG , Bin-feng YANG , Huan-huan YANG, Zhi-yuan ZHAO. An efficient lossy link localization approach for wireless sensor networks[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(5): 689-707.
[10] Zhao-yun Ding, Yan Jia, Bin Zhou, Yi Han, Li He, Jian-feng Zhang. Measuring the spreadability of users in microblogs[J]. Front. Inform. Technol. Electron. Eng., 2013, 14(9): 701-710.
[11] Yun Niu, Li-ji Wu, Yang Liu, Xiang-min Zhang, Hong-yi Chen. A 10 Gbps in-line network security processor based on configurable hetero-multi-cores[J]. Front. Inform. Technol. Electron. Eng., 2013, 14(8): 642-651.
[12] Zhi-bo Wang, Zhi Wang, Hong-long Chen, Jian-feng Li, Hong-bin Li, Jie Shen. HierTrack: an energy-efficient cluster-based target tracking system for wireless sensor networks[J]. Front. Inform. Technol. Electron. Eng., 2013, 14(6): 395-406.
[13] Yan-zhe Che, Kevin Chiew, Xiao-yan Hong, Qiang Yang, Qin-ming He. EDA: an enhanced dual-active algorithm for location privacy preservation in mobile P2P networks[J]. Front. Inform. Technol. Electron. Eng., 2013, 14(5): 356-373.
[14] Parteek Kumar, Rajendra Kumar Sharma. Punjabi DeConverter for generating Punjabi from Universal Networking Language[J]. Front. Inform. Technol. Electron. Eng., 2013, 14(3): 179-196.
[15] Enkhbaatar Tumenjargal, Luubaatar Badarch, Hyeokjae Kwon, Woonchul Ham. Embedded software and hardware implementation system for a human machine interface based on ISOAgLib[J]. Front. Inform. Technol. Electron. Eng., 2013, 14(3): 155-166.