Abstract
This chapter surveys state space modelling approaches for analyzing non-normal time series or longitudinal data. The data situation is the same as in Chapter 6, i.e., categorical, counted or nonnegative data are observed over time. Typical examples are categorized daily rainfall data, the number of monthly polio incidences, or daily measurements on sulfur dioxide. State space models, also termed dynamic models, relate time series observations or longitudinal data {y t } to unobserved “states” α t by an observation model for y t given α t . The states, which may be, e.g., unobserved trend and seasonal components or time-varying covariate effects, are assumed to follow a stochastic transition model. Given the observations {y t }, estimation of states (“filtering” and “smoothing”) is a primary goal of inference.
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
© 1994 Springer Science+Business Media New York
About this chapter
Cite this chapter
Fahrmeir, L., Tutz, G. (1994). State space models. In: Multivariate Statistical Modelling Based on Generalized Linear Models. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-0010-4_8
Download citation
DOI: https://doi.org/10.1007/978-1-4899-0010-4_8
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4899-0012-8
Online ISBN: 978-1-4899-0010-4
eBook Packages: Springer Book Archive