Please use this identifier to cite or link to this item: https://doi.org/10.1080/01621459.2012.706133
Title: Sparse matrix graphical models
Authors: Leng, C. 
Tang, C.Y.
Keywords: Conditional independence
Matrix-variate normal distribution
Penalized likelihood
Sparsistency
Sparsity
Issue Date: 2012
Citation: Leng, C., Tang, C.Y. (2012). Sparse matrix graphical models. Journal of the American Statistical Association 107 (499) : 1187-1200. ScholarBank@NUS Repository. https://doi.org/10.1080/01621459.2012.706133
Abstract: Matrix-variate observations are frequently encountered in many contemporary statistical problems due to a rising need to organize and analyze data with structured information. In this article, we propose a novel sparse matrix graphical model for these types of statistical problems. By penalizing, respectively, two precision matrices corresponding to the rows and columns, our method yields a sparse matrix graphical model that synthetically characterizes the underlying conditional independence structure. Our model is more parsimonious and is practically more interpretable than the conventional sparse vector-variate graphical models. Asymptotic analysis shows that our penalized likelihood estimates enjoy better convergent rates than that of the vector-variate graphical model. The finite sample performance of the proposed method is illustrated via extensive simulation studies and several real datasets analysis. © 2012 American Statistical Association.
Source Title: Journal of the American Statistical Association
URI: http://scholarbank.nus.edu.sg/handle/10635/105381
ISSN: 01621459
DOI: 10.1080/01621459.2012.706133
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