Joint Estimation of Multiple High-dimensional Precision Matrices.
Joint Estimation of Multiple High-dimensional Precision Matrices.
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DOI：
10.5705/ss.2014.256
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发表时间：
2016-04
影响因子：
1.4
通讯作者：
Xie J
中科院分区：
文献类型：
--
作者：
Cai TT;Li H;Liu W;Xie J
出于对不同组织或疾病状态下测量的基因表达数据的分析，我们考虑多个精度矩阵的联合估计，以有效地利用相应图形的部分共享图形结构。该方法基于加权约束最小化问题，可以通过二阶锥规划有效地实现。与单独的估计方法相比，所提出的联合估计方法导致估计器更快地收敛到真正的精度矩阵。在一定的规则性条件下，所提出的过程导致一个确切的图结构恢复的概率趋于1。仿真研究表明，所提出的联合估计方法在图结构恢复方面优于其他方法。通过分析卵巢癌基因表达数据来说明该方法。结果表明，预后不良亚型的患者缺乏凋亡途径中的基因之间的一些重要联系。
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.