@article { ISI:000365144600020,
	title = {S-Estimators for Functional Principal Component Analysis},
	journal = {JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION},
	volume = {110},
	number = {511},
	year = {2015},
	month = {SEP},
	pages = {1100-1111},
	publisher = {AMER STATISTICAL ASSOC},
	type = {Article},
	address = {732 N WASHINGTON ST, ALEXANDRIA, VA 22314-1943 USA},
	abstract = {<p>Principal component analysis is a widely used technique that provides an optimal lower-dimensional approximation to multivariate or functional datasets. These approximations can be very useful in identifying potential outliers among high-dimensional or functional observations. In this article, we propose a new class of estimators for principal components based on robust scale estimators. For a fixed dimension q, we robustly estimate the q-dimensional linear space that provides the best prediction for the data, in the sense of minimizing the sum of robust scale estimators of the coordinates of the residuals. We also study an extension to the infinite-dimensional case. Our method is consistent for elliptical random vectors, and is Fisher consistent for elliptically distributed random elements on arbitrary Hilbert spaces. Numerical experiments show that our proposal is highly competitive when compared with other methods. We illustrate our approach on a real dataset, where the robust estimator discovers atypical observations that would have been missed otherwise. Supplementary materials for this article are available online.</p>
},
	keywords = {Functional data analysis, Robust estimation, Sparse data},
	issn = {0162-1459},
	doi = {10.1080/01621459.2014.946991},
	author = {Boente, Graciela and Salibian-Barrera, Mat{\'\i}as}
}
