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Observation-driven generalized state space models for categorical time series

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  • Zhen, X.
  • Basawa, I.V.

Abstract

Observation-driven state space models are presented for categorical time series as an alternative to the regression type models which are commonly used in the literature. As an application to multi-categorical time series, we present a DNA data analysis and demonstrate the advantages of using state space models.

Suggested Citation

  • Zhen, X. & Basawa, I.V., 2009. "Observation-driven generalized state space models for categorical time series," Statistics & Probability Letters, Elsevier, vol. 79(24), pages 2462-2468, December.
    • Handle: RePEc:eee:stapro:v:79:y:2009:i:24:p:2462-2468
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    References listed on IDEAS

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    1. Fokianos, Konstantinos & Kedem, Benjamin, 1998. "Prediction and Classification of Non-stationary Categorical Time Series," Journal of Multivariate Analysis, Elsevier, vol. 67(2), pages 277-296, November.
    2. Ludwig Fahrmeir & Heinz Kaufmann, 1987. "Regression Models For Non‐Stationary Categorical Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 8(2), pages 147-160, March.
    3. Gary K. Grunwald & Kais Hamza & Rob J. Hyndman, 1997. "Some Properties and Generalizations of Non‐negative Bayesian Time Series Models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(3), pages 615-626.
    4. Michael K. Pitt & Chris Chatfield & Stephen G. Walker, 2002. "Constructing First Order Stationary Autoregressive Models via Latent Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(4), pages 657-663, December.
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