论文标题
基于多元温度的强大图形套索
Robust graphical lasso based on multivariate Winsorization
论文作者
论文摘要
我们建议在Tarr-Muller-Weber框架的上下文中，基于多元温度的强大协方差估计器，以稀疏估计高斯图形模型的精确矩阵。同样，Croux-ollerer的精度矩阵估计器，我们提出的估计器在细胞污染下达到了0.5的最大有限样品分解点。我们进行了广泛的蒙特卡洛模拟研究，以评估我们的表现和当前现有的建议。我们发现，关于精度矩阵的估计和图表的恢复，我们的行为具有竞争性行为。我们证明了所提出的方法在实际应用于乳腺癌数据中的有用性。
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.
