Quantitative Optimal Experimental Design using Global Sensitivity Analysis via Quasi Linearization

Local sensitivity analysis is widely-used in experimental design to improve the precision of the estimated parameters. However, for nonlinear models the local sensitivity values and the experimental design criteria are dependent on the, not yet known, parameter values. Global sensitivity analysis can deal with this situation by taking parameter uncertainty into account for computation of the sensitivity values. However, the existing experimental design criteria cannot easily be applied to the conventional global sensitivity analysis results. One outcome of this is that experimental design involving global sensitivity analysis has mainly focused on identification of influential parameters.

A new global sensitivity analysis technique is presented in this work for the purpose of using this technique for quantitative experimental design. The methodology makes use of quasi linearization and the global sensitivity matrix returned is the design matrix of the linearized model. Due to this, the same experimental criteria that have been developed for quantitative optimal design of linear models can be applied and serve as indicators of desired properties of the parameter estimates. The presented design using global sensitivity analysis is consistent with the popular design involving local sensitivity analysis when the parameter uncertainty is small, however, the technique outperforms local design when applied to models with significant parameter uncertainty.