Abstract: A computer-implemented initial run module processes manifest variable data using computer-defined model specification parameters stored in a database to provide initial estimates of weights that are associated with latent variables. The initial run module employs a unique value-based weighting partial least squares computer-implemented process. A final run module then operates upon the manifest variable data to determine the importance of the predictor values that are then used to control the industrial, manufacturing or commercial process. The final run module implements a unique patient partial least squares regression model utilizing a boosting learning technique.

Abstract: A computer-implemented initial run module processes manifest variable data using computer-defined model specification parameters stored in a database to provide initial estimates of weights that are associated with latent variables. The initial run module employs a unique value-based weighting partial least squares computer-implemented process. A final run module then operates upon the manifest variable data to determine the importance of the predictor values that are then used to control the industrial, manufacturing or commercial process. The final run module implements a unique patient partial least squares regression model utilizing a boosting learning technique.