We propose the use of a robust covariance estimator based on multivariate Winsorization in the context of the Tarr-Muller-Weber framework for sparse estimation of the precision matrix of a Gaussian graphical model. Likewise Croux-Ollerer's precision matrix estimator, our proposed estimator attains the maximum finite sample breakdown point of 0.5 under cellwise contamination. We conduct an extensive Monte Carlo simulation study to assess the performance of ours and the currently existing proposals. We find that ours has a competitive behavior, regarding the the estimation of the precision matrix and the recovery of the graph. We demonstrate the usefulness of the proposed methodology in a real application to breast cancer data.

翻译：我们提议在Tarr-Muller-Weber框架范围内使用基于多变的双赢率的稳健共变量估算器,对高西亚图形模型的精确矩阵进行少许估计。类似地,Croux-Ollerer的精确矩阵估算器,我们提议的估算器在细胞污染下达到了0.5个最高限样本分解点。我们进行了广泛的蒙特卡洛模拟研究,以评估我们和现有提案的绩效。我们发现,在精确矩阵的估算和图的恢复方面,我们的行为具有竞争性。我们展示了拟议方法对乳腺癌数据的实际应用的有用性。