When an ARMAX model is fitted to noise-corrupted data using the prediction error method, biased estimates are obtained. The bias is examined, with emphasis on the situation when the system is almost non-identifiable. In contrast to the case of using an output error model, no general results on the size of the bias seem to apply.
Söderström, T., Soverini, U. (2022). Analyzing the parameter bias when an ARMAX model is fitted to noise-corrupted data. Department of Information Technology, Uppsala University.
Analyzing the parameter bias when an ARMAX model is fitted to noise-corrupted data
Soverini, Umberto
2022
Abstract
When an ARMAX model is fitted to noise-corrupted data using the prediction error method, biased estimates are obtained. The bias is examined, with emphasis on the situation when the system is almost non-identifiable. In contrast to the case of using an output error model, no general results on the size of the bias seem to apply.File in questo prodotto:
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