The Akaike information criterion in weighted regression of immittance data

Abstract

The Akaike Information Criterion (AIC) is a powerful way to distinguish between models. It considers both the goodness-of-fit and the number of parameters in the model, but has been little used for immittance data. Application in the case of weighted complex nonlinear least squares regression, as typically used in fitting impedance or admittance data, is considered here. AIC can be used to compare different weighting schemes as well as different models. These ideas are tested for simulated and real transadmittance data for hydrogen diffusion through an iron foil in a Devanathan-Stachurski cell.