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Model estimation was facilitated by generalized estimating equations, using PROC GENMOD in SAS.
Model estimation was performed using robust Weighted Least Squares (rWLS; estimator = Weighted Least Squares Mean and Variance adjusted (WLSMV)).
Model estimation was performed using robust Weighted Least Squares [ 26] (rWLS; estimator = Weighted Least Squares Mean and Variance adjusted (WLSMV)) procedures in M plus Version 3.13 [ 35].
To validate the results, time-series data were used and model estimation was satisfactory.
The model estimation was undertaken by compiling a database consisting of 10 years of crashes that involved at least one passenger car, and occurred in a work zone.
The model estimation was performed using the software SmartPLS developed by Ringle et al. [31].
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The dedicated core field model estimation is derived directly from the GFZ Reference Internal Magnetic Model (GRIMM) inversion and modeling family.
Five hundred bootstrap replications of the original dataset and model estimation were generated to obtain robust confidence intervals for the final model parameter estimates.
Coefficients from the time series models and the Root Mean Square Error (RMSE) from model estimation are used as input to the Random Forest Classifier (RFC).
The results allow to characterize the domains in which our approach is effective: it performs significantly better than BRUTO when model estimation is challenging.
The analysis and model estimation are performed separately for work tour and non-work tour samples drawn from the 2000 Swiss Microcensus travel survey.
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