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Longitudinal LISREL model estimation from incomplete panel data using the EM algorithm and the Kalman smoother

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  • R. A. R. G. Jansen
  • J. H. L. Oud

Abstract

Longitudinal data sets with the structure T(time points) ×N(subjects) are often incomplete because of data missing for certain subjects at certain time points. The EM algorithm is applied in conjunction with the Kalman smoother for computing maximum likelihood estimates of longitudinal LISREL models from varying missing data patterns. The iterative procedure uses the LISREL program in the M‐step and the Kalman smoother in the E‐step. The application of the method is illustrated by simulating missing data on a data set from educational research.

Suggested Citation

  • R. A. R. G. Jansen & J. H. L. Oud, 1995. "Longitudinal LISREL model estimation from incomplete panel data using the EM algorithm and the Kalman smoother," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 49(3), pages 362-377, November.
  • Handle: RePEc:bla:stanee:v:49:y:1995:i:3:p:362-377
    DOI: 10.1111/j.1467-9574.1995.tb01475.x
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    Cited by:

    1. Johan Oud & Robert Jansen, 2000. "Continuous time state space modeling of panel data by means of sem," Psychometrika, Springer;The Psychometric Society, vol. 65(2), pages 199-215, June.

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