The precision matrix that encodes conditional linear dependency relations among a set of variables forms an important object of interest in multivariate analysis. Sparse estimation procedures for precision matrices such as the graphical lasso (Glasso) gained popularity as they facilitate interpretability, thereby separating pairs of variables that are conditionally dependent from those that are independent (given all other variables). Glasso lacks, however, robustness to outliers. To overcome this problem, one typically applies a robust plug-in procedure where the Glasso is computed from a robust covariance estimate instead of the sample covariance, thereby providing protection against outliers. In this paper, we study such estimators theoretically, by deriving and comparing their influence function, sensitivity curves and asymptotic variances.

翻译：将一组变量之间的有条件线性依赖关系编码为一组变量的精确矩阵是多变量分析的一个重要对象。 图形 lasso (Glasso) 等精确矩阵的粗略估计程序由于便于解释而越来越受欢迎, 从而将有条件依赖的变量与独立变量的对应变量分开( 列出所有其他变量 ) 。 Glasko 缺乏对外部线的稳健性。 为了解决这一问题, 通常采用一个强大的插件程序, 即Glaso 是从稳健的共变量估计数而不是样本的共变量中计算, 从而提供保护, 防止外延。 在本文中, 我们从理论上研究这些估算器, 其影响功能、 灵敏曲线 和 随机差异 。</s>