A Method to Estimate the Statistical Significance of a Correlation When the Data Are Serially Correlated

When analyzing pairs of time series, one often needs to know whether a correlation is statistically significant. If the data are Gaussian distributed and not serially correlated, one can use the results of classical statistics to estimate the significance. While some techniques can handle non-Gaussi...

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Bibliographic Details
Published in:Journal of climate 1997-09, Vol.10 (9), p.2147-2153
Main Author: Ebisuzaki, Wesley
Format: Article
Language:English
Subjects:
Autocorrelation
Correlations
Earth, ocean, space
Estimation methods
Exact sciences and technology
External geophysics
Geophysics. Techniques, methods, instrumentation and models
Meteorology
Statistical estimation
Statistical significance
Statistics
Time series
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Summary:When analyzing pairs of time series, one often needs to know whether a correlation is statistically significant. If the data are Gaussian distributed and not serially correlated, one can use the results of classical statistics to estimate the significance. While some techniques can handle non-Gaussian distributions, few methods are available for data with nonzero autocorrelation (i.e., serially correlated). In this paper, a nonparametric method is suggested to estimate the statistical significance of a computed correlation coefficient when serial correlation is a concern. This method compares favorably with conventional methods.
ISSN:0894-8755
1520-0442
DOI:10.1175/1520-0442(1997)010<2147:amtets>2.0.co;2