Abstract
Some applications of the state space models that were described in Chapter 5 are presented in this chapter. In particular, the modeling of the famous Canadian lynx data by an AR state space model, the modeling of irregularly spaced data and an example of the decomposition of an observed time series into a signal, background noise and observation noise are shown.
This is a preview of subscription content, log in via an institution to check access.
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
© 1996 Springer Science+Business Media New York
About this chapter
Cite this chapter
Kitagawa, G., Gersch, W. (1996). Applications of Linear Gaussian State Space Modeling. In: Smoothness Priors Analysis of Time Series. Lecture Notes in Statistics, vol 116. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-0761-0_7
Download citation
DOI: https://doi.org/10.1007/978-1-4612-0761-0_7
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-94819-5
Online ISBN: 978-1-4612-0761-0
eBook Packages: Springer Book Archive