Robust surrogate measurement correction using generalised additive model

The generalised additive model (GAM) was developed as a prognostic tool for the investigation of data set trends. However, it is also proposed as a viable framework for the development of surrogate measurement corrections for instrumental data sets. This hypothesis is supported by an example application, the development of a robust vehicle exhaust flow measurement correction for a flow meter with a known measurement error.