Abstract
This chapter surveys state space and hidden Markov modelling approaches for analyzing time series or longitudinal data, spatial data, and spatiotemporal data. Responses are generally non-Gaussian, in particular, categorical, counted or nonnegative. State space and hidden Markov models have the common feature that they relate responses to unobserved “states” or “parameters” by an observation model. The states, which may represent, e.g., an unobserved temporal or spatial trend or time-varying covariate effects, are assumed to follow a latent or “hidden” Markov model.
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
© 2001 Springer Science+Business Media New York
About this chapter
Cite this chapter
Fahrmeir, L., Tutz, G. (2001). State Space and Hidden Markov Models. In: Multivariate Statistical Modelling Based on Generalized Linear Models. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-3454-6_8
Download citation
DOI: https://doi.org/10.1007/978-1-4757-3454-6_8
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-2900-6
Online ISBN: 978-1-4757-3454-6
eBook Packages: Springer Book Archive