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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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Published in: | The Annals of statistics 2012-08, Vol.40 (4), p.1973-1977 |
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Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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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.) |
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ISSN: | 0090-5364 2168-8966 |
DOI: | 10.1214/12-AOS979 |