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Sparse matrix graphical models
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Leng, Chenlei and Tang, C. Y. (2012) Sparse matrix graphical models. Journal of the American Statistical Association, Vol.107 (No.499). pp. 1187-1200. doi:10.1080/01621459.2012.706133 ISSN 0162-1459.
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Official URL: http://dx.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.
Item Type: | Journal Article | ||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||
Journal or Publication Title: | Journal of the American Statistical Association | ||||
Publisher: | American Statistical Association | ||||
ISSN: | 0162-1459 | ||||
Official Date: | October 2012 | ||||
Dates: |
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Volume: | Vol.107 | ||||
Number: | No.499 | ||||
Page Range: | pp. 1187-1200 | ||||
DOI: | 10.1080/01621459.2012.706133 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access |
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