Joint Estimation of Multiple High-dimensional Precision Matrices.
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 constrainedℓ∞/ℓ1minimization, 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.
Professor of Statistics, Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104.
T Tony
TT
Cai
Professor of Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104.
Hongzhe
H
Li
Professor, Department of Mathematics, Institute of Natural Sciences and MOE-LSC, Shanghai Jiao Tong University, Shanghai, China.
Weidong
W
Liu
Assistant Professor, Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC 27707.
Jichun
J
Xie
Constrained optimization; Convergence rate; Graph recovery; Precision matrices; Second-order cone programming; Sparsity