Abstract
Accuracy evaluation is an integral part of parameter estimation by least-squaresanalysis. We present an analysis of error propagation when the measurement erroris a second-order autoregressive process. When the measurement correlation isneglected, least-squares procedures underestimate the uncertainty. In order toscale up the uncertainty, we investigated autoregressive measurement errors,calculated their correlation, and assessed the estimate uncertainty in terms of thesampling frequency. Our results, which are amenable to approximations andnumerical computations, show clearly how correlation influences error propagationand are useful in devising strategies for optimal measurement design.
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