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The CFA revealed that a one-factor model with correlated residuals was the more parsimonious model.
It is found that when developing hydrologic regression models, application of the MCCV is likely to result in a more parsimonious model than the LOO.
We show that a more parsimonious model, based solely on the well-established general factor of cognitive ability, fits their data at least as well.
The newly developed GA based approach resulted in a significantly more parsimonious model (BIC = −1.54) compared to ANN (BIC = −1.36) and NLR (BIC = −1.32) models.
The results show that the proposed model had a moderate fit to the observed data, and a more parsimonious model was found to have a better fit.
The proposed framework exploits that the IDR can be reduced to a nonparametric function which relies only on a few linear combinations of the original covariates, hence leading to a more parsimonious model.
In consequence, they were excluded in order to have a more parsimonious model.
AIC penalizes complex models with more parameters; smaller values indicate a more parsimonious model.
The more parsimonious model (model 2) fitted just as well as the first model, ΔΧ2 SB (5) = 2.362, p = .797.797
In general, similar results were obtained from a more parsimonious model for the clustering based on marginal models with robust standard errors (i.e., a generalized estimating equation framework).
Therefore, all examined variables have important combined explanatory effects on the level of under-reporting in Europe, which could not be fully explained by a more parsimonious model.
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