GENERALIZED S-ESTIMATORS
GENERALIZED S-ESTIMATORS
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DOI：
10.1080/01621459.1994.10476867
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发表时间：
1994-12-01
影响因子：
3.7
通讯作者：
HOSSJER, O
中科院分区：
文献类型：
--
作者：
CROUX, C;ROUSSEEUW, PJ;HOSSJER, O
本文介绍了一种新的正分解回归方法，称为广义S估计(或GS-估计)，它是基于残差尺度的广义M-估计的最小化。我们将这类GS估计与通常的S估计进行了比较，包括最小中值平方估计。结果表明，GS估计比S估计获得了更高的效率，但代价是略微增加了最坏情况的偏差。我们研究了GS-估计的崩溃点、最大偏差曲线和影响函数。我们还给出了一个计算GS-估计量的算法，并将其应用于实际数据和模拟数据。
In this article we introduce a new type of positive-breakdown regression method, called a generalized S-estimator (or GS-estimator), based on the minimization of a generalized M-estimator of residual scale. We compare the class of GS-estimators with the usual S-estimators, including least median of squares. It turns out that GS-estimators attain a much higher efficiency than S-estimators, at the cost of a slightly increased worst-case bias. We investigate the breakdown point, the maxbias curve, and the influence function of GS-estimators. We also give an algorithm for computing GS-estimators and apply it to real and simulated data.