The Graphical Horseshoe Estimator for Inverse Covariance Matrices
The Graphical Horseshoe Estimator for Inverse Covariance Matrices
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DOI：
10.1080/10618600.2019.1575744
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发表时间：
2019-04-29
影响因子：
2.4
通讯作者：
Bhadra, Anindya
中科院分区：
文献类型：
--
作者：
Li, Yunfan;Craig, Bruce A.;Bhadra, Anindya
本文利用马蹄先验，提出了高维多元正态数据逆协方差矩阵的一种新的估计。建议的图形马蹄估计有吸引力的性质相比，其他流行的估计，如图形套索和图形平滑剪切绝对偏差。最突出的好处是，当真正的逆协方差矩阵是稀疏的，图形马蹄提供的估计与采样模型的信息偏差很小。在一定条件下，图马蹄先验下的后验均值也可以几乎无偏。除了这些理论结果，我们还提供了一个完整的吉布斯采样器实现我们的估计。MATLAB代码可以从github下载。图形马蹄估计器与模拟和人类基因网络数据分析中的现有技术相比具有优势。可以在网上找到。
We develop a new estimator of the inverse covariance matrix for high-dimensional multivariate normal data using the horseshoe prior. The proposed graphical horseshoe estimator has attractive properties compared to other popular estimators, such as the graphical lasso and the graphical smoothly clipped absolute deviation. The most prominent benefit is that when the true inverse covariance matrix is sparse, the graphical horseshoe provides estimates with small information divergence from the sampling model. The posterior mean under the graphical horseshoe prior can also be almost unbiased under certain conditions. In addition to these theoretical results, we also provide a full Gibbs sampler for implementing our estimator. MATLAB code is available for download from github at . The graphical horseshoe estimator compares favorably to existing techniques in simulations and in a human gene network data analysis. for this article are available online.