The Heisenberg modelling procedure and application to nonlinear modelling

The main features of the Heisenberg modelling procedure are presented. These include weighing schemes, the simultaneous expansion of X and X+, orthogonality requirements, stopping rules and objectives of modelling. These are applied to linear modelling in order to show how these tools work. We show how the criteria of linear PLS regression can be extended to include nonlinear regression. It is shown how the Heisenberg modelling procedure applies to modelling general nonlinear models. An important aspect of the Heisenberg modelling procedure is that it provides tools for outlier detection, sensitivity analysis, dimension analysis and variable (data) selection for each of its procedures.