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A State‐Space EM Algorithm for Longitudinal Data

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  • Gloria Icaza
  • Richard Jones

Abstract

An exact EM algorithm is developed for Gaussian longitudinal mixed models in state‐space form. These models include between‐subject random effects as well as within‐subject serial correlation and possibly observational error. Data for each subject may be equally spaced with missing observations, or unequally spaced with different observation times for different subjects. The method uses the Kalman filter and smoothing algorithm to obtain the conditional expectations of the unobserved data given the observations used in the E step of the EM algorithm. Maximum likelihood estimates of the parameters are obtained wtihout the need to use nonlinear optimization routines. Using simulations, the method is shown to give identical results to maximum likelihood methods that use nonlinear optimization.

Suggested Citation

  • Gloria Icaza & Richard Jones, 1999. "A State‐Space EM Algorithm for Longitudinal Data," Journal of Time Series Analysis, Wiley Blackwell, vol. 20(5), pages 537-550, September.
    • Handle: RePEc:bla:jtsera:v:20:y:1999:i:5:p:537-550
    DOI: 10.1111/1467-9892.00155
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