Theoretical Limitations of Allan Variance-based Regression for Time Series Model Estimation
This letter formally proves the statistical inconsistency of the Allan variance-based estimation of latent (composite) model parameters. This issue has not been sufficiently investigated and highlighted since it is a technique that is still being widely used in practice, especially within the engine...
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Veröffentlicht in: | IEEE signal processing letters 2016-05, Vol.23 (5), p.597-601 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | This letter formally proves the statistical inconsistency of the Allan variance-based estimation of latent (composite) model parameters. This issue has not been sufficiently investigated and highlighted since it is a technique that is still being widely used in practice, especially within the engineering domain. Indeed, among others, this method is frequently used for inertial sensor calibration, which often deals with latent time series models and practitioners in these domains are often unaware of its limitations. To prove the inconsistency of this method, we first provide a formal definition and subsequently deliver its theoretical properties, highlighting its limitations by comparing it with another statistically sound method. |
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ISSN: | 1070-9908 1558-2361 |
DOI: | 10.1109/LSP.2016.2541867 |