In this paper, distributed estimation of high-dimensional sparse precision matrix is proposed based on the debiased D-trace loss penalized lasso and the hard threshold method when samples are distributed into different machines for transelliptical graphical models. At a certain level of sparseness, this method not only achieves the correct selection of non-zero elements of sparse precision matrix, but the error rate can be comparable to the estimator in a non-distributed setting. The numerical results further prove that the proposed distributed method is more effective than the usual average method.

中文翻译：

---

椭圆图形模型的高维稀疏精度矩阵的高效分布估计

本文提出了一种基于去偏D迹损失惩罚套索和硬阈值法的高维稀疏精度矩阵的分布式估计方法，该方法将样本分配到跨椭圆图形模型的不同机器中。在一定程度的稀疏性下，此方法不仅可以正确选择稀疏精度矩阵的非零元素，而且错误率可以与非分布式设置中的估计器相比。数值结果进一步证明了所提出的分布式方法比通常的平均方法更有效。