A topologically valid construction of depth for functional data

Alicia Nieto-Reyes and Heather Battey

Journal of Multivariate Analysis, 2021, vol. 184, issue C

Abstract: Numerous problems remain in the construction of statistical depth for functional data. Issues stem largely from the absence of a well-conceived notion of symmetry. The present paper proposes a topologically valid notion of symmetry for distributions on functional metric spaces and a corresponding notion of depth. The latter is shown to satisfy the axiomatic definition of functional depth introduced by Nieto-Reyes and Battey (2016).

Keywords: Functional data analysis; Statistical depth; Symmetry (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.jmva.2021.104738

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