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Functional regression on remote sensing data in oceanography

Author
Acar, N.; Delicado, P.; Basarir, G.; Caballero, I.
Type of activity
Journal article
Journal
Environmental and ecological statistics
Date of publication
2018-06-01
Volume
25
Number
2
First page
277
Last page
304
DOI
https://doi.org/10.1007/s10651-018-0405-7 Open in new window
Project funding
Bridging the gap between Statistics and Data Science
Repository
http://hdl.handle.net/2117/120600 Open in new window
URL
https://link.springer.com/article/10.1007%2Fs10651-018-0405-7 Open in new window
Abstract
The aim of this study is to propose the use of a functional data analysis approach as an alternative to the classical statistical methods most commonly used in oceanography and water quality management. In particular we consider the prediction of total suspended solids (TSS) based on remote sensing (RS) data. For this purpose several functional linear regression models and classical non-functional regression models are applied to 10 years of RS data obtained from medium resolution imaging spectr...
Citation
Acar, N., Delicado, P., Basarir, G., Caballero, I. Functional regression on remote sensing data in oceanography. "Environmental and ecological statistics", 1 Juny 2018, vol. 25, núm. 2, p. 277-304.
Keywords
Exponential regression models, Functional linear regression models, Functional partial least squares, Functional principal components, Remote sensing data
Group of research
ADBD - Analysis of Complex Data for Business Decisions

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