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DISCUSSION: LATENT VARIABLE GRAPHICAL MODEL SELECTION VIA CONVEX OPTIMIZATION

The authors want to congratulate the authors for a thought-provoking and very interesting paper. Sparse modeling of the concentration matrix has enjoyed popularity in recent years. It has been framed as a computationally convenient convex ...-constrained estimation problem in Yuan and Lin (2007) and...

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Bibliographic Details
Published in:The Annals of statistics 2012-08, Vol.40 (4), p.1973-1977
Main Authors: Lauritzen, Steffen, Meinshausen, Nicolai
Format: Article
Language:English
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Summary:The authors want to congratulate the authors for a thought-provoking and very interesting paper. Sparse modeling of the concentration matrix has enjoyed popularity in recent years. It has been framed as a computationally convenient convex ...-constrained estimation problem in Yuan and Lin (2007) and can be applied readily to higher-dimensional problems. The authors argue that the sparsity of the concentration matrix is for many applications more plausible after the effects of a few latent variables have been removed. The most attractive point about their method is surely that it is formulated as a convex optimization problem. Latent variable fitting and sparse graphical modeling of the conditional distribution of the observed variables can then be obtained through a single fitting procedure.(ProQuest: ... denotes formulae/symbols omitted.)
ISSN:0090-5364
2168-8966
DOI:10.1214/12-AOS979