Abstract
This thesis proposes some new iterative local modeling algorithms for the multivariate approximation problem (mapping from R P to R). Partial Least Squares Regression (PLS)is used as the local linear modeling technique. The local models are interpolated by means of normalized Gaussian weight functions, providing a smooth total nonlinear model. The
algorithms are tested on both artificial and real world set of data, yielding good predictions compared to other linear and nonlinear techniques.