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A partial regression model shows the beta convergence of regions taking into account only the change in TSR due to the component under consideration.
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The results for partial regression models are reported; thus, F- and P-values reflect whether the model (R) significantly improved when the covariates of interest were added to the initial model.
Tong et al. [33] considered consistency and normality of HD estimators for the partial linear regression model.
On the other hand, the prediction results of second-order partial polynomial regression model as elite empirical one showed the weakness of this model comparing ANN and ANFIS models.
In this paper we consider some non-parametric goodness-of-fit statistics for testing the partial Koziol Green regression model.
Principal component analysis and partial least regression models were developed using the first derivative DRIFT spectra (400 4000 cm−1) to predict fructose and glucose sugars.
Partial least squares discriminant analysis (PLS-DA) is a variant of the classic partial least squares regression model that aims to discriminate groups of samples [ 22, 23].
A partial least squares regression model was calibrated and validated with a totally independent data set.
The partial least squares regression model gave a mean error lower than 5% for all components presented in the mixture.
An alternative is available in the partial ordinal logistic regression model which relaxes the assumption of proportional odds, allowing the parameters to vary across the level of the dependent variables, but yielding a less parsimonious model.
More specifically, optimal bone shape and mineral density features are selected based on their predictive power and used within a partial least square regression model to estimate the unknown trabecular micro-architecture within the anatomical models of new subjects.
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