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The marginal fittings of univariate maximum entropy distributions and the trivariate data fitting based on normal copula fit the data well.
This paper derives two asymptotically efficient estimators, one for concentric circles fitting and the other for concentric ellipses fitting, based on the weighted equation error formulation and non-linear parameter transformation.
Finally, the lower eyelid is localized using parabolic curve fitting, based on gray level segmentation.
Then absorption coefficients are computed by curve fitting based on the least square method.
Nonlinear curve fitting based on the least squares algorithm was used to optimize parameters in the proposed RDII model.
The purpose of interpolation is to recover this missing information in between by employing a polynomial curve fitting based on linear model estimator.
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Multivariate logistic regression was fitted based on backward hierarchical elimination approach and the minimal model was reported.
The fits based on the reference priors visually coincide, whereas the fit based on π U is slightly different.
11 to 15 are fitted based on total 105 experimental data in this study.
The model was fitted based on projections over pairs of consecutive measurements.
The proposed SEM is fitted based on a partial least square (PLS) estimation.
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