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In this work we present a novel method to build a multivariate calibration model for NIR spectra, i.e. genetic algorithm radial basis function network in wavelet domain (WT-GA RBFN), WT-GA RBFNines the advantages of whichet transform and geneticombinesthe.
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build a univariate regression for each independent variable, build a multivariate regression model including all variables with p < 0.05.
build a multivariate regression model including all variables with p < 0.05. .
Using just this subset of predictors, build a multivariate classifier, using all of the samples except those in fold k.
There were insufficient data to build a multivariate model.
Logistic regression was used to build a multivariate model of predictors of PSA testing.
Factors with meaningful correlations (P≤0·1) were used to build a multivariate regression analysis model.
Univariate associations with a p-value < 0.25 were used to build a multivariate model using a backward elimination method.
To build a multivariate model, we used forward selection to evaluate the additive effects of risk factors.
ANN is a multivariate calibration method capable of modeling complex functions.
A multivariate calibration strategy based on inverse least squares approach was employed for quantification.
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