A characterization of elliptical distributions and some optimality properties of principal components for functional data

Graciela Boente, Matías Salibián Barrera and David E. Tyler

Journal of Multivariate Analysis, 2014, vol. 131, issue C, 254-264

Abstract: As in the multivariate setting, the class of elliptical distributions on separable Hilbert spaces serves as an important vehicle and reference point for the development and evaluation of robust methods in functional data analysis. In this paper, we present a simple characterization of elliptical distributions on separable Hilbert spaces, namely we show that the class of elliptical distributions in the infinite-dimensional case is equivalent to the class of scale mixtures of Gaussian distributions on the space. Using this characterization, we establish a stochastic optimality property for the principal component subspaces associated with an elliptically distributed random element, which holds even when second moments do not exist. In addition, when second moments exist, we establish an optimality property regarding unitarily invariant norms of the residuals covariance operator.

Keywords: Elliptical distributions; Functional data analysis; Principal components (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (14)

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DOI: 10.1016/j.jmva.2014.07.006

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