Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.csda.2013.09.002
Title: Edge detection in sparse Gaussian graphical models
Authors: Luo, S.
Chen, Z. 
Keywords: Edge detection
Extended Bayesian information criterion
Graphical model
Selection consistency
Sequential selection
Issue Date: 2014
Citation: Luo, S., Chen, Z. (2014). Edge detection in sparse Gaussian graphical models. Computational Statistics and Data Analysis 70 : 138-152. ScholarBank@NUS Repository. https://doi.org/10.1016/j.csda.2013.09.002
Abstract: In this paper, we consider the problem of detecting edges in a Gaussian graphical model. The problem is equivalent to the identification of non-zero entries of the concentration matrix of a normally distributed random vector. Following the methodology initiated in Meinshausen and Bühlmann (2006), we tackle the problem through regression models where each component of the random vector is regressed on the remaining components. We adapt a method called SLasso cum EBIC (sequential LASSO cum extended Bayesian information criterion) recently developed in Luo and Chen (2011) for feature selection in sparse regression models to suit the special nature of the concentration matrix, and propose two approaches, dubbed SR-SLasso and JR-SLasso, for the identification of non-zero entries of the concentration matrix. Comprehensive numerical studies are conducted to compare the proposed approaches with other available competing methods. The numerical studies demonstrate that the proposed approaches are more accurate than the other methods for the identification of non-zero entries of the concentration matrix. © 2013 Elsevier B.V. All rights reserved.
Source Title: Computational Statistics and Data Analysis
URI: http://scholarbank.nus.edu.sg/handle/10635/105101
ISSN: 01679473
DOI: 10.1016/j.csda.2013.09.002
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