@article{10902/24538,
year = {2021},
month = {7},
url = {http://hdl.handle.net/10902/24538},
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).},
organization = {The work was supported by a UK Engineering and Physical Sciences Research Council research fellowship (to HSB) and a Spanish Ministerio de Ciencia, Innovación y Universidades grant MTM2017-86061-C2-2-P (to AN-R).},
publisher = {Academic Press Inc.},
publisher = {Journal of Multivariate Analysis, Volume 184, July 2021},
title = {A topologically valid construction of depth for functional data},
author = {Nieto Reyes, Alicia and Battey, Heather},
}