Projection-Pursuit Approach to Robust Dispersion Matrices and Principal Components: Primary Theory and Monte Carlo

1 September 1985

journal article
research article
Published by JSTOR in Journal of the American Statistical Association

Vol. 80 (391) , 759
https://doi.org/10.2307/2288497

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.

Keywords

COVARIANCE MATRIX
CORRELATION MATRIX
BREAKDOWN POINT
ROTATIONAL EQUIVARIANCE
MULTIVARIATE DATA ANALYSIS
CONSISTENCY

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