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We suggest a new Chance Variance (C V) criterion to model the returns characterized by fuzzy random variables.
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On the other hand, the observed weak dependence of the quiescent background on time, especially at high energies where it is most important for the analysis of celestial sources, lets us use other observations (cleaned of the flares using exactly the same criteria) to model the background.
Applying these criteria to models (mathcal {M}_{2}) and (mathcal {M}_{3}) one can identify the financially constrained component as the one that has the largest standard deviation.
We used a weighted least squares (WLS) criterion to assess model fit and applied our method to Belgian ILI data over six influenza seasons.
We used deviance and the deviance information criterion to appraise model fit.
The criterion to select model parameter(s) was the average classification accuracy computed on hold-out samples across resampling results.
Akaike information criterion (AIC) was used as the criterion to compare model-fitting results in the model selection algorithm.
When developing prognostic or predictive models an information criterion approach to model selection is attractive, particularly as some of the comparisons of interest are not naturally nested.
As in a previous study, 30 we used the Bayes information criterion to compare models.
The criterion to compare models was the accuracy of estimated breeding values for the purebred validation population, calculated as the correlation between true and estimated breeding values.
Since the full likelihood of the data is not specified in GEE models, likelihood-based criteria to assess model fit are not available.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com