Sentence examples for original predictor from inspiring English sources

The final design in terms of coded and original predictor variables is given in Table 1.

The underlying idea is to maximize the amount of variance of the original predictor variables that is retained by the components in order to reduce the variance involved in estimating the forecast model.

It utilizes the singular value decomposition of the normalized predictors, and allows bigger deviation from zero to the regression parameters of the orthonormalized predictors which are known to explain the original predictor variability most, i.e., have biggest singular values.

The components are linear combinations of the original predictor variables.

We rank the original predictor variables with respect to their VIP.

It projects the original predictor data into a latent lower dimensional space and thus maximizes the covariance with the response variable.

Then, several sparse PLS (SPLS) methods are proposed to simultaneously conduct prediction and variable selection via sparsely combining original predictors.

PLS is based on latent components that are linear combinations of all of the original predictors, so it automatically employs all predictors regardless of their relevance.

PLS is based on latent components which are linear combinations of the original predictors, it automatically employs all predictors regardless of their relevance.

The variable has a one-to-one relationship with the finite space state coded by : one specific value of represents a specific combination of the values of the original predictors, that is, a "bin" into which the data is categorized.

This led to a significant reduction in fit and an increase in the AIC (α2 = 7.07, df = 1, p = 0.008, AIC = 2282.68), indicating that the best model contains all of the original predictors with the exception of perceived knowledge.