Projection-Pursuit Approach to Robust Dispersion Matrices and Principal Components: Primary Theory and Monte Carlo
提出一种基于投影追踪的协方差/相关矩阵和主成分的稳健估计方法，兼具旋转等变性和高崩溃点，蒙特卡洛模拟显示其性能优于其他稳健方法。
Abstract This article proposes and discusses a type of new robust estimators for covariance/correlation matrices and principal components via projection-pursuit techniques. The most attractive advantage of the new procedures is that they are of both rotational equivariance and high breakdown point. Besides, they are qualitatively robust and consistent at elliptic underlying distributions. The Monte Carlo study shows that the best of the new estimators compare favorably with other robust methods. They provide as good a performance as M-estimators and somewhat better empirical breakdown properties. Key Words: Covariance matrixCorrelation matrixBreakdown pointRotational equivarianceMultivariate data analysisConsistency