Correlation confidence limits for unevenly sampled data
File(s)
Accepted version
Author(s)
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.
Date Issued
2016-09-28
Date Acceptance
2016-09-25
Citation
Computers & Geosciences, 2016, 104, pp.120-124
ISSN
0098-3004
Publisher
Elsevier
Start Page
120
End Page
124
Journal / Book Title
Computers & Geosciences
Volume
104
Copyright Statement
© 2016, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor
British Council (UK)
Grant Number
ICECAP-2
Subjects
Science & Technology
Technology
Physical Sciences
Computer Science, Interdisciplinary Applications
Geosciences, Multidisciplinary
Computer Science
Geology
Unevenly sampled data
Autocorrelation
Bootstrapping
Gaussian kernel method
Confidence limits
TIME-SERIES
METHANESULFONIC-ACID
EAST ANTARCTICA
BOOTSTRAP
COEFFICIENT
INTERVALS
04 Earth Sciences
08 Information And Computing Sciences
09 Engineering
Geochemistry & Geophysics
Publication Status
Published