Robust estimates of principal components are developed using appropriate deΓΏnitions of multivariate signs and ranks. Simulations and a data example are used to compare these methods to the regular method and one based on the minimum-volume-ellipsoid estimate of the covariance matrix. The sign and ra
Asymptotic theory for robust principal components
β Scribed by Graciela Boente
- Publisher
- Elsevier Science
- Year
- 1987
- Tongue
- English
- Weight
- 615 KB
- Volume
- 21
- Category
- Article
- ISSN
- 0047-259X
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
A robust method called robust principal components regression based on principal sensitivity vectors (RPPSV) is developed for outlier detection in regression. The method is evaluated by its outlier detection ability and the root mean square error of prediction (RMSEP) for a test set using simulated
This paper is concerned with a study of robust estimation in principal component analysis. A class of robust estimators which are characterized as eigenvectors of weighted sample covariance matrices is proposed, where the weight functions recursively depend on the eigenvectors themselves. Also, a fe