Other variables with problematic distributions can be transformed by a method based on the empirical cumulative distribution function.
The normal marginals method yielded problematic distributions of the test statistic (5) when simulating the H 1-distribution, which may also be explained: If we split along the score r v (using the median, for example), score distributions in the subsamples will inevitably differ, causing the observed differences.
Modeling species whose distributions span large environmental or habitat variation may be problematic because distribution models tend to have higher rates of omission error (i.e. underprediction) in the predicted species distributions [ 2, 17, 18].
And the claim that owners of resources should receive the marginal contribution of their resources to production is especially problematic: The existing distribution of such resources is tainted by its historical evolution.
This can be very problematic because the distribution of similarity scores largely depends on the specific type of fingerprint that is used and the reference compound for which the fingerprint is calculated.
While reliable on small scale, this strategy can prove problematic when the distribution of food is patchy.
RRV is problematic because the distribution range of its main host, the raccoon (Procyon lotor), is linked to human presence, which increases the risk of spread to domestic animals and humans (Cullingham et al. 2009).
This approach was adopted to avoid using the average of the central values, which can be problematic if the distribution of the variable under study (ω in this case) is asymmetric.
This can be especially problematic in skewed distributions, such as we find with SOD scores (Fig. 1).
