Linear Gaussian State Space Modeling

  • Genshiro Kitagawa
  • Will Gersch
Part of the Lecture Notes in Statistics book series (LNS, volume 116)


Linear Gaussian state space modeling is treated in this chapter. The prediction, filtering and smoothing formulas in the standard Kalman filter are shown. Model identification or, computation of the likelihood of the model is also treated. Some of the well known state space models that are used in this book as well as state space modeling of missing observations and a state space model for unequally spaced time series are shown. The final section is a discussion of the information square root filter/smoother, that we use in linear Gaussian state space seasonal decomposition modeling in Chapter 9. Not necessarily linear - not necessarily Gaussian state space modeling is treated in Chapter 6. A variety of illustrative examples of linear state space modeling is shown in Chapter 7.


State Space Kalman Filter State Space Modeling ARMA Model State Space Representation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 1996

Authors and Affiliations

  • Genshiro Kitagawa
    • 1
  • Will Gersch
    • 2
  1. 1.The Institute of Statistical MathematicsTokyoJapan
  2. 2.Department of Information and Computer ScienceUniversity of HawaiiHonoluluUSA

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