• Brockwell, P. J., and R. A. Davis, 1991: Time Series: Theory and Methods. Springer-Verlag, 577 pp.

  • Davis, R. E., 1976: Predictability of sea surface temperatures and sea level pressure anomalies over the North Pacific Ocean. J. Phys. Oceanogr.,6, 249–266.

  • Fraser, A. M., 1989: Reconstructing attractors from scalar time series: A comparison of singular system and redundancy criteria. Physica D,34, 391–404.

  • Gershenfeld, N. A., and A. S. Weigend, 1993: The future of time series: Learning and understanding. Time Series Prediction: Forecasting the Future and Understanding the Past, A. S. Weigend and N. A. Gershenfeld, Eds., Addison-Wesley, 1–70.

  • Kaplan, D. T., 1993: A geometrical statistic for detecting deterministic dynamics (data sets A, B, C, D). Time Series Prediction: Forecasting the Future and Understanding the Past, A. S. Weigend and N. A. Gershenfeld, Eds., Addison-Wesley, 415–428.

  • Katz, R. W., 1982: Statistical evaluation of climate experiments with general circulation models: A parametric time series modeling approach. J. Atmos. Sci.,39, 1446–1455.

  • Theiler, J., P. S. Linsay, and D. M. Rubin, 1993: Detecting nonlinearity in data with long coherence times (data set E). Time Series Prediction: Forecasting the Future and Understanding the Past, A. S. Weigend and N. A. Gershenfeld, Eds., Addison-Wesley, 429–455.

  • Thiébaux, H. J., and F. W. Zwiers, 1984: The interpretation and estimation of effective sample size. J. Climate Appl. Meteor.,23, 800–811.

  • ——, and M. A. Pedder, 1987: Spatial Objective Analysis: With Applications in Atmospheric Science. Academic Press, 299 pp.

  • Trenberth, K. E., 1984: Some effects of finite sample size and persistence on meteorological statistics. Part I: Autocorrelations. Mon. Wea. Rev.,112, 2359–2368.

  • Zwiers, F. W., 1990: The effect of serial correlation on statistical inferences made with resampling procedures. J. Climate,3, 1452–1461.

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

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  • 1 Research and Data Systems Corp., Greenbelt, Maryland
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Abstract

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.

Corresponding author address: Dr. Wesley Ebisuzaki, Climate Prediction Center, NOAA/NCEP,W/NP52, Washington, DC 20233.

Abstract

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.

Corresponding author address: Dr. Wesley Ebisuzaki, Climate Prediction Center, NOAA/NCEP,W/NP52, Washington, DC 20233.

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