A procedure called GOLPE is suggested in order to detect those variables which increase the predictivity of PLS models. The procedure is based on evaluating the predictive power of a number of PLS models built by different combinations of variables selected according to a factorial design strategy. Examples are given of the efficiency of this variable selection procedure, which shows how these predictive PLS models are better than those obtained by all variables and better than the corresponding ordinary regression model
Purpose: Partial least squares (PLS) has been introduced as a “causal-predictive” approach to struct...
<p>The feature group G4 contains 62 individual variables and the model of G4 gives the best performa...
A new method is proposed for comparing all predictors in a multiple regression model. This method ge...
Ten techniques used for selection of useful predictors in multivariate calibration and in other case...
he standard deviation of prediction errors (SDEP) is used to evaluate and compare the predictive abi...
The selection of a descriptor, X, is crucial for improving the interpretation and prediction accurac...
A common problem in applied regression analysis is to select the variables that enter a linear regre...
n advanced variable selection procedure, called GOLPE, aimed at obtaining PLS regression models with...
With advanced capability in data collection, applications of linear regression analysis now often in...
Predictive model selection metrics are used to select models with the highest out-of-sample predict...
Researchers who make predictions from educational data are interested in choosing the best regressio...
The object of research is the task of constructing a linear regression model that arises in the proc...
A new method for the elimination of useless predictors in multivariate regression problems is propos...
It is non-trivial to select the appropriate prediction technique from a variety of existing techniqu...
Regression models with good fitting but no predictive ability are sometimes chance correlations and ...
Purpose: Partial least squares (PLS) has been introduced as a “causal-predictive” approach to struct...
<p>The feature group G4 contains 62 individual variables and the model of G4 gives the best performa...
A new method is proposed for comparing all predictors in a multiple regression model. This method ge...
Ten techniques used for selection of useful predictors in multivariate calibration and in other case...
he standard deviation of prediction errors (SDEP) is used to evaluate and compare the predictive abi...
The selection of a descriptor, X, is crucial for improving the interpretation and prediction accurac...
A common problem in applied regression analysis is to select the variables that enter a linear regre...
n advanced variable selection procedure, called GOLPE, aimed at obtaining PLS regression models with...
With advanced capability in data collection, applications of linear regression analysis now often in...
Predictive model selection metrics are used to select models with the highest out-of-sample predict...
Researchers who make predictions from educational data are interested in choosing the best regressio...
The object of research is the task of constructing a linear regression model that arises in the proc...
A new method for the elimination of useless predictors in multivariate regression problems is propos...
It is non-trivial to select the appropriate prediction technique from a variety of existing techniqu...
Regression models with good fitting but no predictive ability are sometimes chance correlations and ...
Purpose: Partial least squares (PLS) has been introduced as a “causal-predictive” approach to struct...
<p>The feature group G4 contains 62 individual variables and the model of G4 gives the best performa...
A new method is proposed for comparing all predictors in a multiple regression model. This method ge...