A topologically valid construction of depth for functional data

Nieto Reyes, Alicia; Battey, Heather

doi:10.1016/j.jmva.2021.104738

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Identificadores

URI: http://hdl.handle.net/10902/24538

DOI: 10.1016/j.jmva.2021.104738

ISSN: 0047-259X

ISSN: 1095-7243

Fecha

2021-07

Derechos

Publicado en

Journal of Multivariate Analysis, Volume 184, July 2021

Editorial

Academic Press Inc.

Enlace a la publicación

https://doi.org/10.1016/j.jmva.2021.104738

Palabras clave

Functional data analysis

Statistical depth

Symmetry

Resumen/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).

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© 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons. org/licenses/by-nc-nd/4.0/).

Excepto si se señala otra cosa, la licencia del ítem se describe como © 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons. org/licenses/by-nc-nd/4.0/).