On an Additive Semigraphoid Model for Statistical Networks With Application to Pathway Analysis

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We introduce a nonparametric method for estimating non-Gaussian graphical models based on a new statistical relation called additive conditional independence, which is a three-way relation among random vectors that resembles the logical structure of conditional independence. Additive conditional independence allows us to use one-dimensional kernel regardless of the dimension of the graph, which not only avoids the curse of dimensionality but also simplifies computation. It also gives rise to a parallel structure to the Gaussian graphical model that replaces the precision matrix by an additive precision operator. The estimators derived from additive conditional independence cover the recently introduced nonparanormal graphical model as a special case, but outperform it when the Gaussian copula assumption is violated. We compare the new method with existing ones by simulations and in genetic pathway analysis. Supplementary materials for this article are available online.
Publisher
AMER STATISTICAL ASSOC
Issue Date
2014-07
Language
English
Article Type
Article
Citation

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, v.109, no.507, pp.1188 - 1204

ISSN
0162-1459
DOI
10.1080/01621459.2014.882842
URI
http://hdl.handle.net/10203/264311
Appears in Collection
MA-Journal Papers(저널논문)
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