ON THE PREDICTIVE PERFORMANCE OF BIASED REGRESSION METHODS AND MULTIPLE LINEAR-REGRESSION

Kowalski, K.G., 1990. On the predictive performance of biased regression methods and multiple linear regression. Chemometrics and Intelligent Laboratory Systems, 9: 177-184. The predictive performance of three commonly used biased regression methods (ridge, principal components and partial least squares) and multiple linear regression (ordinary least squares) using classical model selection techniques are evaluated on five data sets published in the chemical and statistical literature. For these five data sets, the degree of collinearity among the regressors varies considerably. For each data set, cross-validation is performed and the prediction error sum of squares (PRESS) is computed to assess the predictive performance of each method. The results show that multiple linear regression using reduced models obtained by classical methods of model selection performed better (lower PRESS) than the three commonly used biased regression methods. © 1990.