Abstract
When dealing with multivariate data robust principal component analysis (PCA), like classical PCA, searches for directions with maximal dispersion of the data projected on it. Instead of using the variance as a measure of dispersion, a robust scale estimator s n may be used in the maximization problem. In this paper, we review some of the proposed approaches to robust functional PCA including one which adapts the projection pursuit approach to the functional data setting.
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Acknowledgements
This research was partially supported by Grants 276 from the Universidad de Buenos Aires, pip 216 from conicet and pict 821 from anpcyt at Buenos Aires, Argentina. The authors wish to thank three anonymous referees for valuable comments which led to an improved version of the original paper.
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Bali, J.L., Boente, G. (2014). Robust Functional Principal Component Analysis. In: Pacheco, A., Santos, R., Oliveira, M., Paulino, C. (eds) New Advances in Statistical Modeling and Applications. Studies in Theoretical and Applied Statistics(). Springer, Cham. https://doi.org/10.1007/978-3-319-05323-3_4
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DOI: https://doi.org/10.1007/978-3-319-05323-3_4
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