论文标题
惩罚和混合M-估计器的分解点的协方差
Breakdown points of penalized and hybrid M-estimators of covariance
论文作者
论文摘要
我们介绍了一类多元散射的混合M-估计器，这些散射类似于流行的空间符号协方差矩阵（SSCM），具有高分子分解点。我们还表明，可以将SSCM视为此类的极端成员。与SSCM不同，但是像散射的常规M-估计器一样，这类新的估计器考虑了数据云轮廓的形状，以减少观测。
We introduce a class of hybrid M-estimators of multivariate scatter which, analogous to the popular spatial sign covariance matrix (SSCM), possess high breakdown points. We also show that the SSCM can be viewed as an extreme member of this class. Unlike the SSCM, but like the regular M-estimators of scatter, this new class of estimators takes into account the shape of the contours of the data cloud for downweighting observations.
