Influence functions and outlier detection under the common principal components model: A robust approach
Influence functions and outlier detection under the common principal components model: A robust approach
复制标题
共同主成分模型下的影响函数和异常值检测：一种稳健的方法
DOI：
10.1093/biomet/89.4.861
复制
发表时间：
2002
期刊：
影响因子：
2.7
通讯作者：
I. M. Rodrigues
中科院分区：
文献类型：
--
作者：
G. Boente;A. Pires;I. M. Rodrigues
几组多变量观测的共同主成分模型假设主轴相等，但组间沿这些轴的方差不同。得到了一种通用主成分模型下插入估计和投影追踪估计的影响函数。渐近方差是由它们推导出来的。使用部分影响函数可以检测异常值。版权所有Biometrika Trust 2002，牛津大学出版社。
The common principal components model for several groups of multivariate observations assumes equal principal axes but different variances along these axes among the groups. Influence functions for plug-in and projection-pursuit estimates under a common principal component model are obtained. Asymptotic variances are derived from them. Outlier detection is possible using partial influence functions. Copyright Biometrika Trust 2002, Oxford University Press.