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

Bing Li, Hyonho Chun, Hongyu Zhao

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1188-1204
Number of pages17
JournalJournal of the American Statistical Association
Volume109
Issue number507
DOIs
StatePublished - Sep 2014

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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