Gong, M., Miller, C. and Scott, E. (2015) Functional PCA for Remotely Sensed Lake Surface Water Temperature Data. In: Spatial Statistics 2015: Emerging Patterns, Avignon, France, 9-12 Jun 2015, pp. 127-130. (doi: 10.1016/j.proenv.2015.05.015)
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Abstract
Functional principal component analysis is used to investigate a high-dimensional surface water temperature data set of Lake Victoria, which has been produced in the ARC-Lake project. Two different perspectives are adopted in the analysis: modelling temperature curves (univariate functions) and temperature surfaces (bivariate functions). The latter proves to be a better approach in the sense of both dimension reduction and pattern detection. Computational details and some results from an application to Lake Victoria data are presented.
Item Type: | Conference Proceedings |
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Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Scott, Professor Marian and Miller, Professor Claire |
Authors: | Gong, M., Miller, C., and Scott, E. |
College/School: | College of Science and Engineering > School of Mathematics and Statistics > Statistics |
ISSN: | 1878-0296 |
Copyright Holders: | Copyright © 2015 The Authors |
First Published: | First published in Procedia Environmental Sciences 26:127-130 |
Publisher Policy: | Reproduced under a Creative Commons License |
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