Correlations between continuous variables were analysed by Pearson's (symmetrical distribution) or Spearman's (asymmetrical distribution) correlation coefficient.
The computing and statistical technology underpinning Monte Carlo processes has improved since those pioneering works and now enables asymmetrical distributions, correlations between variables and the risk of fire to be recognised using different distribution shapes for different inputs or outputs and preserving multivariate relationships.
It is therefore demonstrated that the copula is a more appropriate dependence measure capable of modeling asymmetrical tails whereas correlation is not appropriate to model skewed data.
A strong correlation between the asymmetrical visibility threshold and texture complexity is revealed from the subjective experiments, and a texture-based asymmetrical visibility threshold model (TAVT) is established.
Although there is not a straightforward biological explanation for it [ 27, 28], this inverse correlation reflects an asymmetrical evolution of PRDM paralogs and is compatible with their progressive sub- and neo-functionalization [ 7, 8].
We found that (1) reliable flow pattern is associated with high persistence in the Holling models, while the LV models predict no consistent correlations; (2) in asymmetrical situations, persistence is always much higher (in both LV and Holling models); and (3) the predictions of Holling models (versus LV models) are much less sensitive to local perturbations.
If the discretization approach is to have any merit, over correlation, it should identify asymmetrical relationships.
This difference could be due to the truncation of microarray data (the threshold 7.0 was applied, see Materials and Methods) because of which the distributions of expression data were asymmetrical (fig. 2), causing problems for correlation analysis.
The funnel plot shows a slightly asymmetrical distribution of points; however the rank-correlation test of Begg's (p=0.095) and Egger's (p=0.112) showed a non-significant publication bias.
However, unlike correlation function, the CCF is not asymmetrical, that is, ρ xy (k) ≠ ρ xy (−k).
