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Erschienen in:
2010 | OriginalPaper | Buchkapitel
State Space Methods for Latent Trajectory and Parameter Estimation by Maximum Likelihood
verfasst von : Jacques J. F. Commandeur, Siem Jan Koopman, Kees van Montfort
Erschienen in: Longitudinal Research with Latent Variables
Verlag: Springer Berlin Heidelberg
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We review Kalman filter and related smoothing methods for the latent trajectory in multivariate time series. The latent effects in the model are modelled as vector unobserved components for which we assume particular dynamic stochastic processes. The parameters in the resulting multivariate unobserved components time series models will be estimated by maximum likelihood methods. Some essential details of the state space methodology are discussed in this chapter. An application in the modelling of traffic safety data is presented to illustrate the methodology in practice.