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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1989 Springer Science+Business Media New York
About this chapter
Cite this chapter
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
Download citation
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