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The results of model estimation are shown in Table 1.
Also, the experience gained practically in a way that all procedures associated with model estimation are mimicked analytically.
For comparison, the results of the beam lobe model estimation are also presented, based on the approach of a spatial moving least squares (MLS) method [21].
The analysis and model estimation are performed separately for work tour and non-work tour samples drawn from the 2000 Swiss Microcensus travel survey.
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 trust region method which originated from the Levenberg Marquardt (LM) algorithm for mixed effect model estimation are considered in the context of second level functional magnetic resonance imaging (fMRI) data analysis.
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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)).
The dedicated core field model estimation is derived directly from the GFZ Reference Internal Magnetic Model (GRIMM) inversion and modeling family.
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].
Five hundred bootstrap replications of the original dataset and model estimation were generated to obtain robust confidence intervals for the final model parameter estimates.
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