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A robust principal component analysis

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  • Ruymgaart, F. H.

Abstract

A robust principal component analysis for samples from a bivariate distribution function is described. The method is based on robust estimators for dispersion in the univariate case along with a certain linearization of the bivariate structure. Besides the continuity of the functional defining the direction of the suitably modified principal axis, we prove consistency of the corresponding sequence of estimators. Asymptotic normality is established under some additional conditions.

Suggested Citation

  • Ruymgaart, F. H., 1981. "A robust principal component analysis," Journal of Multivariate Analysis, Elsevier, vol. 11(4), pages 485-497, December.
    • Handle: RePEc:eee:jmvana:v:11:y:1981:i:4:p:485-497
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    Cited by:

    1. Jolliffe, Ian, 2022. "A 50-year personal journey through time with principal component analysis," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    2. Salvatore Greco & Alessio Ishizaka & Menelaos Tasiou & Gianpiero Torrisi, 2019. "On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(1), pages 61-94, January.

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