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A new class of generalized correlation coefficients that contains the Pearson and Kendall statistics as special cases was defined by Chinchilli et al. (2005) and applied to the estimation of correlations coefficients within the context of 2×2 cross-over designs for clinical trials.
However, the literature on robust estimation of correlations for time series is scarce and mainly focused on autocovariances and autocorrelations.
Indeed the method would then allow estimation of correlations between R at different spatial locations.
Bivariate analyses allowed estimation of correlations between traits.
Random regression analysis enables estimation of correlations between any pairs of age points.
The Pearson correlation coefficients for corresponding probe-probe pairs were then computed as the estimation of correlations.
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This article is divided into three sections as follows: Section "Phase-space reconstruction of chaotic time series" describes the chaotic theory on phase space reconstruction employed to obtain the estimation of correlation dimension.
The permutation procedure and estimation of correlation coefficient are described in the next section.
This measure of dissimilarity can be further developed into statistical distance when stable estimation of correlation is available.
There are statistical questions, other than the estimation of correlation coefficients, that may be relatively insusceptible to the effect of signal aggregation.
General sample size and power considerations for estimation of correlation and differences between groups for continuous outcome variables were performed using Stata IC version 11.
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