4.6 Article

STRUCTURAL SIMILARITY AND DIFFERENCE TESTING ON MULTIPLE SPARSE GAUSSIAN GRAPHICAL MODELS

期刊

ANNALS OF STATISTICS
卷 45, 期 6, 页码 2680-2707

出版社

INST MATHEMATICAL STATISTICS
DOI: 10.1214/17-AOS1539

关键词

Common substructure; false discovery rate; Gaussian graphical model; high dimensional; structural similarity; structural difference

资金

  1. NSFC [11322107, 11431006]
  2. Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning
  3. Shanghai Shuguang Program
  4. Shanghai Youth Talent Support Program
  5. 973 Program [2015CB856004]

向作者/读者索取更多资源

We present a new framework on inferring structural similarities and differences among multiple high-dimensional Gaussian graphical models (GGMs) corresponding to the same set of variables under distinct experimental conditions. The new framework adopts the partial correlation coefficients to characterize the potential changes of dependency strengths between two variables. A hierarchical method has been further developed to recover edges with different or similar dependency strengths across multiple GGMs. In particular, we first construct two-sample test statistics for testing the equality of partial correlation coefficients and conduct large-scale multiple tests to estimate the substructure of differential dependencies. After removing differential substructure from original GGMs, a follow-up multiple testing procedure is used to detect the substructure of similar dependencies among GGMs. In each step, false discovery rate is controlled asymptotically at a desired level. Power results are proved, which demonstrate that our method is more powerful on finding common edges than the common approach that separately estimates GGMs. The performance of the proposed hierarchical method is illustrated on simulated datasets.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据