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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.
The model estimation is made in three successive steps.
The basic test of any model estimation is examination of the sign of model coefficients.
Since D,I, and F data are involved, model estimation is a nonlinear optimization problem.
The better the residual and correlation noise model estimation is, the better the fusion process works.
Convergence-guaranteed efficient algorithms are derived for the proposed methods, and the relationship between the generalized Gaussian and Student's t distributions in the source model estimation is revealed.
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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].
Five hundred bootstrap replications of the original dataset and model estimation were generated to obtain robust confidence intervals for the final model parameter estimates.
The results of model estimation are shown in Table 1.
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