Principal component analysis based on robust estimators of the covariance or correlation matrix: influence functions and efficiencies
基于稳健协方差或相关矩阵估计的主成分分析：影响函数与效率
A robust principal component analysis can be easily performed by computing the eigenvalues and eigenvectors of a robust estimator of the covariance or correlation matrix. In this paper we derive the influence functions and the corresponding asymptotic variances for these robust estimators of eigenvalues and eigenvectors. The behaviour of several of these estimators is investigated by a simulation study. It turns out that the theoretical results and simulations favour the use…