Cramér-Rao Lower Bounds (CRLB) are widely applied to characterize the minimum possible variance of metabolite amplitude parameters estimated by linear combination modeling. It has been argued that calculating the CRLB in the absence of baseline terms cannot adequately capture error but that the distribution of spectral baseline modeling parameters themselves cannot be sufficiently represented by this index. In this work we test the practical implications of these principles by treating baselines as linear combinations of polynomials to show that CRLB can under some circumstances offer precision estimates on spectral baseline shapes, notably to the improvement of metabolite CRLB accuracy.
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