Confidence intervals for high-dimensional inverse covariance estimation
Confidence intervals for high-dimensional inverse covariance estimation
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DOI：
10.1214/15-ejs1031
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发表时间：
2015-01-01
影响因子：
1.1
通讯作者：
van de Geer, Sara
中科院分区：
文献类型：
--
作者：
Jankova, Jana;van de Geer, Sara
我们提出了一种方法，在高维设置稀疏精度矩阵的低维参数的统计推断。我们的方法导致一个非稀疏估计的精度矩阵，其条目具有高斯极限分布。在真精度矩阵项的稀疏性假设和正则性条件下，分析了亚高斯观测下新估计的渐近性质。保持去稀疏化估计量的不变保证了相关联的图形模型中的边选择。所提出的方法的性能示出在模拟研究。
We propose methodology for statistical inference for low-dimensional parameters of sparse precision matrices in a high-dimensional setting. Our method leads to a non-sparse estimator of the precision matrix whose entries have a Gaussian limiting distribution. Asymptotic properties of the novel estimator are analyzed for the case of sub-Gaussian observations under a sparsity assumption on the entries of the true precision matrix and regularity conditions. Thresholding the de-sparsified estimator gives guarantees for edge selection in the associated graphical model. Performance of the proposed method is illustrated in a simulation study.