Optimally weighted L2 distances for spatially dependent functional data

Romano, E., Diana, A., Miller, C. and O'Donnell, R. (2020) Optimally weighted L2 distances for spatially dependent functional data. Spatial Statistics, 39, 100468. (doi: 10.1016/j.spasta.2020.100468)

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In recent years, in many application fields, extracting information from data in the form of functions is of most interest rather than investigating traditional multivariate vectors. Often these functions have complex spatial dependences that need to be accounted for using appropriate statistical analysis. Spatial Functional Statistics presents a fruitful analytics framework for this analysis. The definition of a distance measure between spatially dependent functional data is critical for many functional data analysis tasks such as clustering and classification. For this reason, and based on the specific characteristics of functional data, several distance measures have been proposed in the last few years. In this work we develop a weighted distance for spatially dependent functional data, with an optimized weight function. Assuming a penalized basis representation for the functional data, we consider weight functions depending also on the spatial location in two different situations: a classical georeferenced spatial structure and a connected network one. The performance of the proposed distances are compared using standard metrics applied to both real and simulated data analysis.

Item Type:Articles
Glasgow Author(s) Enlighten ID:O'Donnell, Dr Ruth and Miller, Professor Claire
Authors: Romano, E., Diana, A., Miller, C., and O'Donnell, R.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Spatial Statistics
ISSN (Online):2211-6753
Published Online:11 September 2020
Copyright Holders:Copyright © 2020 Elsevier B.V.
First Published:First published in Spatial Statistics 39:100468
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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