Estimating Pearson's Correlation Coefficient with Bootstrap Confidence Interval from Serially Dependent Time Series

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Persistence matters: Estimation of the statistical significance of paleoclimatic reconstruction statistics from autocorrelated time series (2012, methodology): “…as their assumptions are not satisfied. ore flexible bootstrap and Monte-Carlo techniques (Efron and ibshirani, 1986) can often perform better than classical methods ith special regard to climatic (von Storch and Zwiers, 1999) or aleoclimatic (e.g. Mudelsee, 2003; Young et al., 2000) persistence.”
More accurate, calibrated bootstrap confidence intervals for estimating the correlation between two time series (2014, methodology): “BCa confidence intervals were used in the previous version of PearsonT (Mudelsee 2003).”
Summer-drought constrains the phenology and growth of two coexisting Mediterranean oaks with contrasting leaf habit: implications for their persistence and reproduction (2009, methodology): “…between growth and climatic variables, we estimated the corrected 95% confidence intervals for the correlation coefficients using stationary bootstrap estimates with an average block length proportional to the maximum estimated autocorrelation of the data (Park and Lee 2001; Mudelsee 2003).”