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The first is a two-stage least-squares approach.
Surrogate models are built using a Moving Least Squares approach.
Wang et al. [20] demonstrate that the fuzzy geometric approach outperforms the traditional least squares approach.
Ordinary least squares approach was used to fit the multivariable linear regression models.
The parameters p, q, and s were determined using a least squares approach.
In addition, rule consequent parameters are optimized using a local least square approach.
More recently, in [8], a channel estimator is proposed based on a robust least-squares approach.
In order to obtain the estimates, classical least-squares approach is employed as follows: (30).
Multivariable regression model was fit using the ordinary least squares approach.
The Linear regression analysis model (or meta-regression) follows the ordinary least squares approach.
A multivariate calibration strategy based on inverse least squares approach was employed for quantification.
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