Regression, Transfer Function and Noise Models

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Bayesian Forecasting and Dynamic Models

Part of the book series: Springer Series in Statistics ((SSS))

Abstract

Polynomial and seasonal models provide two basic and very widely used classes of DLMs, both, of course, TSDLMs. A third important class is that based on regression relationships; the three classes combined via the principle of superposition provide for the vast majority of forms of behaviour encountered in practice for which linear models can provide adequate descriptions. Here we describe the theory and analysis of regression DLMs and important related models.

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© 1989 Springer Science+Business Media New York

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West, M., Harrison, J. (1989). Regression, Transfer Function and Noise Models. In: Bayesian Forecasting and Dynamic Models. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-9365-9_9

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  • DOI: https://doi.org/10.1007/978-1-4757-9365-9_9

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4757-9367-3

  • Online ISBN: 978-1-4757-9365-9

  • eBook Packages: Springer Book Archive

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