The refinement of PLS models by iterative weighting of predictor variables and objects

The flexibility of PLS algorithm can be used to assign suitable weights to predictors or to objects or to both predictors and objects. Weights of predictors are obtained from the regression coefficients and the standard deviation. Weights of objects are obtained from the prediction residuals. By iterative weighting, the regression models are refined and a steady state is attained, where useless predictors and anomalous objects are cancelled, and a very economical model is obtained. The predictive ability and stability of this final model are better than those of the original model with all the available predictors and objects.