Joint Estimation of Multiple High-dimensional Precision Matrices.

Journal Article (Journal Article)

Motivated by analysis of gene expression data measured in different tissues or disease states, we consider joint estimation of multiple precision matrices to effectively utilize the partially shared graphical structures of the corresponding graphs. The procedure is based on a weighted constrained ℓ∞/ℓ1 minimization, which can be effectively implemented by a second-order cone programming. Compared to separate estimation methods, the proposed joint estimation method leads to estimators converging to the true precision matrices faster. Under certain regularity conditions, the proposed procedure leads to an exact graph structure recovery with a probability tending to 1. Simulation studies show that the proposed joint estimation methods outperform other methods in graph structure recovery. The method is illustrated through an analysis of an ovarian cancer gene expression data. The results indicate that the patients with poor prognostic subtype lack some important links among the genes in the apoptosis pathway.

Full Text

Duke Authors

Cited Authors

  • Cai, TT; Li, H; Liu, W; Xie, J

Published Date

  • April 2016

Published In

Volume / Issue

  • 26 / 2

Start / End Page

  • 445 - 464

PubMed ID

  • 28316451

Pubmed Central ID

  • PMC5351783

International Standard Serial Number (ISSN)

  • 1017-0405

Digital Object Identifier (DOI)

  • 10.5705/ss.2014.256

Language

  • eng

Conference Location

  • China (Republic : 1949- )