Correlation confidence limits for unevenly sampled data

TY - JOUR

T1 - Correlation confidence limits for unevenly sampled data

AU - Roberts, J

AU - Curran, Mark

AU - Poynter, S.

AU - Moy, A.

AU - van Ommen, T.D.

AU - Vance, T.R.

AU - Tozer, C.

AU - Graham, F.S.

AU - Young, David A.

AU - Plummer, CE

AU - Pedro, Joel Benjamin

AU - Blankenship, D.

AU - Siegert, Martin J.

PY - 2017/7/1

Y1 - 2017/7/1

N2 - 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.

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.

U2 - 10.1016/j.cageo.2016.09.011

DO - 10.1016/j.cageo.2016.09.011

M3 - Journal article

SN - 0098-3004

VL - 104

SP - 120

EP - 124

JO - Computers & Geosciences

JF - Computers & Geosciences

IS - C

ER -