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

@article{citeulike:6057047, 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.}, author = {Li, Guoying and Chen, Zhonglian}, citeulike-article-id = {6057047}, citeulike-linkout-0 = {http://dx.doi.org/10.2307/2288497}, citeulike-linkout-1 = {http://www.jstor.org/stable/2288497}, doi = {10.2307/2288497}, issn = {01621459}, journal = {Journal of the American Statistical Association}, number = {391}, pages = {759--766}, posted-at = {2009-11-02 18:05:48}, priority = {2}, publisher = {American Statistical Association}, title = {Projection-Pursuit Approach to Robust Dispersion Matrices and Principal Components: Primary Theory and Monte Carlo}, url = {http://dx.doi.org/10.2307/2288497}, volume = {80}, year = {1985} }

TY - JOUR ID - citeulike:6057047 L3 - citeulike-article-id:6057047 N2 - 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. IS - 391 JF - Journal of the American Statistical Association SN - 01621459 EP - 766 TI - Projection-Pursuit Approach to Robust Dispersion Matrices and Principal Components: Primary Theory and Monte Carlo PB - American Statistical Association VL - 80 SP - 759 AU - Li, Guoying AU - Chen, Zhonglian PY - 1985/// UR - http://dx.doi.org/10.2307/2288497 ER -