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Some robust estimates of principal components

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Author Info
Marden, John I.
Abstract

Robust estimates of principal components are developed using appropriate definitions 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 rank procedures are quite robust unless there is severe contamination, in which case the minimum-volume-ellipsoid estimate is preferable.

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File URL: http://www.sciencedirect.com/science/article/B6V1D-3XHH69M-4/2/d56a864aaf1d6c33ec1252ebe548d5e8
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Publisher Info
Article provided by Elsevier in its journal Statistics & Probability Letters.

Volume (Year): 43 (1999)
Issue (Month): 4 (July)
Pages: 349-359
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Handle: RePEc:eee:stapro:v:43:y:1999:i:4:p:349-359

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Related research
Keywords: Principal components Multivariate analysis Multivariate ranks Multivariate signs Robust estimation Minimum-volume-ellipsoid estimator;

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. N. Locantore & J. Marron & D. Simpson & N. Tripoli & J. Zhang & K. Cohen & Graciela Boente & Ricardo Fraiman & Babette Brumback & Christophe Croux & Jianqing Fan & Alois Kneip & John Marden & Daniel P, 1999. "Robust principal component analysis for functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 8(1), pages 1-73, June. [Downloadable!] (restricted)
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This page was last updated on 2009-12-30.


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