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We assessed the models by comparing four indicators of fit, including the chi-square/degrees of freedom ratio (χ2/df), comparative fit index (CFI), non-normed fit index (NNFI), and root mean square error of approximation (RMSEA).
Dr. Silverman evaluates his models by comparing them with the actual motives and behaviors observed for various groups.
The numerical results are validated for the models by comparing them with experimental data.
It is normal practice to validate simulation models by comparing their numerical results to experimental data.
Then we calibrate the coarser models by comparing these similar outputs to the measurements.
This study deals with the evaluation of these models by comparing the simulation results with experimental data.
This paper analyses asymmetric discrete choice models by comparing specifications expressed as deviations from the reference point and specifications expressed in absolute values.
Both 2D and 3D models will predict ground surface roughness more precisely and stably than traditional models by comparing with a dressing lead single-factor experiment.
It is used to benchmark seismic data-processing algorithms, to design acquisition parameters, and to validate subsurface models by comparing modeled data with measured data.
This work evaluates the efficacy of ensemble models by comparing individual numerical models in terms of their performance in predicting the compressive strength of HPC.
We validate the code in a wide range of dark matter models by comparing the relic density results from MadDM to the existing tools and literature.
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