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Many of these approaches involve mathematical and statistical complexities that limit their use routinely.
However, natural stimuli typically contain strong correlations and are spherically asymmetric (i.e. stimulus intensities are not symmetrically distributed around the mean), and these statistical complexities can bias receptive field (RF) estimates when standard techniques such as spike-triggered averaging or reverse correlation are used.
However, integrating data from heterogeneous sources brings many challenges due to experimental, computational or statistical complexities.
This is partly because there are additional statistical complexities associated with calculating confidence intervals for ratios such as the ICER.
Furthermore, to concentrate on substantially altered sites and to reduce the statistical complexities associated with large numbers of tests being performed on a small sample set, CpG loci that were considered to be non-variant (β values <0.1 or >0.9 in all samples) were eliminated from the analyses.
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It is shown that the MPR statistical complexity measure is capable to quantify this randomness improvement.
In a recent paper a new statistical complexity measure [Phys. Lett. A 311 (2003) 126] has been defined.
Recourse is made to a plane that has as coordinates (i) the Shannon entropy and (ii) a form of the statistical complexity measure.
In this work we have used Statistical Complexity measures.
Several statistical complexity measures can be defined which aim to clarify our argument.
That is correct and this observation motivates the introduction of two new statistical complexity measures.
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