Correlation confidence limits for unevenly sampled data

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Title: Correlation confidence limits for unevenly sampled data
Author(s): Roberts, J
Curran, M
Poynter, S
Moy, A
Van Ommen, T
Vance, T
Tozer, C
Graham, F
Young, D
Plummer, C
Pedro, J
Blankenship, D
Siegert, M
Item Type: Journal Article
Abstract: Estimation of correlation with appropriate uncertainty limits for scientific data that are potentially serially correlated is a common problem made seriously challenging especially when data are sampled unevenly in space and/or time. Here we present a new, robust method for estimating correlation with uncertainty limits between autocorrelated series that does not require either resampling or interpolation. The technique employs the Gaussian kernel method with a bootstrapping resampling approach to derive the probability density function and resulting uncertainties. The method is validated using an example from radar geophysics. Autocorrelation and error bounds are estimated for an airborne radio-echo profile of ice sheet thickness. The computed limits are robust when withholding 10%, 20%, and 50% of data. As a further example, the method is applied to two time-series of methanesulphonic acid in Antarctic ice cores from different sites. We show how the method allows evaluation of the significance of correlation where the signal-to-noise ratio is low and reveals that the two ice cores exhibit a significant common signal.
Publication Date: 28-Sep-2016
Date of Acceptance: 25-Sep-2016
ISSN: 0098-3004
Publisher: Elsevier
Start Page: 120
End Page: 124
Journal / Book Title: Computers & Geosciences
Volume: 104
Sponsor/Funder: British Council (UK)
Funder's Grant Number: ICECAP-2
Copyright Statement: © 2016, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Keywords: Science & Technology
Physical Sciences
Computer Science, Interdisciplinary Applications
Geosciences, Multidisciplinary
Computer Science
Unevenly sampled data
Gaussian kernel method
Confidence limits
04 Earth Sciences
08 Information And Computing Sciences
09 Engineering
Geochemistry & Geophysics
Publication Status: Published
Appears in Collections:Centre for Environmental Policy
Faculty of Natural Sciences

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