Imperial College London
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ProfessorMartinSiegert

Faculty of Natural Sciences, The Grantham Institute for Climate Change

Visiting Professor
 
 
 
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Contact

 

+44 (0)20 7594 9666m.siegert Website

 
 
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Assistant

 

Ms Gosia Gayer +44 (0)20 7594 9666

 
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Location

 

Grantham Directors OfficeSherfield BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Roberts:2016:10.1016/j.cageo.2016.09.011,
author = {Roberts, J and Curran, M and Poynter, S and Moy, A and van, Ommen T and Vance, T and Tozer, C and Graham, F and Young, D and Plummer, C and Pedro, J and Blankenship, D and Siegert, M},
doi = {10.1016/j.cageo.2016.09.011},
journal = {Computers & Geosciences},
pages = {120--124},
title = {Correlation confidence limits for unevenly sampled data},
url = {http://dx.doi.org/10.1016/j.cageo.2016.09.011},
volume = {104},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Roberts,J
AU  - Curran,M
AU  - Poynter,S
AU  - Moy,A
AU  - van,Ommen T
AU  - Vance,T
AU  - Tozer,C
AU  - Graham,F
AU  - Young,D
AU  - Plummer,C
AU  - Pedro,J
AU  - Blankenship,D
AU  - Siegert,M
DO  - 10.1016/j.cageo.2016.09.011
EP  - 124
PY  - 2016///
SN  - 0098-3004
SP  - 120
TI  - Correlation confidence limits for unevenly sampled data
T2  - Computers & Geosciences
UR  - http://dx.doi.org/10.1016/j.cageo.2016.09.011
UR  - http://hdl.handle.net/10044/1/41384
VL  - 104
ER  -
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