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More recently, other criteria have been suggested, especially approaches based on statistical criteria such as randomization and orthogonality.
Assessment of candidate models was primarily based on statistical criteria.
Consequently, our study focused on the content-related validity evidence for the construct 'commercial competence' and not on statistical criteria.
Also, to identify which optimization approach has the best performance, the comparison study based on statistical criteria is implemented.
Here, we rely on statistical criteria defined in the spectral domain such as the shape of the Lowes Mauersberger power spectrum, the degree correlation, and the azimuthal power spectrum [see Finlay et al. (2010), for definitions].
Our approach considers multiple possible groupings, choosing the "best" set based on statistical criteria, and correcting by permutation.
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Alternatively, or in addition, one can use data-driven methods to define these groupings based only on statistical criterion; e.g., all possible allele frequencies, all possible subsets of rare variants, or a "step-up" approach we propose here.
One general consensus is, however, that the number of groups should not be chosen only on the basis of statistical criteria but also on the notion of clinical relevance [ 39].
Candidate tags were selected on the basis of statistical criteria (see Materials and methods) limiting our analysis to tags whose average intensity was higher than log210.
Furthermore, a best-fit approach was adopted: three different regression models (probit, logit, and Weibull) were fitted independently to the same pooled data set, and the best fit was selected on the basis of statistical criteria (Scholze et al. 2001).
We performed statistical concentration response analyses in the same way for all compounds and for the mixture by applying a best-fit procedure: various nonlinear regression models were fitted independently to the same data set, and we selected the best fit on the basis of statistical criteria.
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