Asymptotic distributions of principal components based on robust dispersions
基于稳健离散度的主成分渐近分布
Algebraically, principal components can be defined as the eigenvalues and eigenvectors of a covariance or correlation matrix, but they are statistically meaningful as successive projections of the multivariate data in the direction of maximal variability. An attractive alternative in robust principal component analysis is to replace the classical variability measure, i.e. variance, by a robust dispersion measure. This projection‐pursuit approach was first proposed in Li &…