Statistical Science

A Topologically Valid Definition of Depth for Functional Data

Alicia Nieto-Reyes and Heather Battey

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Abstract

The main focus of this work is on providing a formal definition of statistical depth for functional data on the basis of six properties, recognising topological features such as continuity, smoothness and contiguity. Amongst our depth defining properties is one that addresses the delicate challenge of inherent partial observability of functional data, with fulfillment giving rise to a minimal guarantee on the performance of the empirical depth beyond the idealised and practically infeasible case of full observability. As an incidental product, functional depths satisfying our definition achieve a robustness that is commonly ascribed to depth, despite the absence of a formal guarantee in the multivariate definition of depth. We demonstrate the fulfillment or otherwise of our properties for six widely used functional depth proposals, thereby providing a systematic basis for selection of a depth function.

Article information

Source
Statist. Sci., Volume 31, Number 1 (2016), 61-79.

Dates
First available in Project Euclid: 10 February 2016

Permanent link to this document
https://projecteuclid.org/euclid.ss/1455115914

Digital Object Identifier
doi:10.1214/15-STS532

Mathematical Reviews number (MathSciNet)
MR3458593

Zentralblatt MATH identifier
06946212

Keywords
Functional data multivariate statistics partial observability robustness statistical depth

Citation

Nieto-Reyes, Alicia; Battey, Heather. A Topologically Valid Definition of Depth for Functional Data. Statist. Sci. 31 (2016), no. 1, 61--79. doi:10.1214/15-STS532. https://projecteuclid.org/euclid.ss/1455115914


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