This paper accurately quantifies the way in which noise induced estimation errors are dependent on model structure, underlying system frequency response, measurement noise and input excitation. This exposes several new principles. In particular, it is shown here that when employing Output–Error model structures in a prediction-error framework, then the ensuing estimate variability in the freque...

Abstract: This paper establishes a method for quantifying variance error in cases where the input spectral density has a rational factorisation. Compared to previous work which has involved asymptotic-in-model-order techniques and yielded expressions which are only approximate for finite orders, the quantifications provided here are exact for finite model order, although they still only apply a...