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Longitudinal LISREL model estimation from incomplete panel data using the EM algorithm and the Kalman smoother
Author(s) -
Jansen R. A. R. G.,
Oud J. H. L.
Publication year - 1995
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/j.1467-9574.1995.tb01475.x
Subject(s) - lisrel , kalman filter , missing data , data set , algorithm , longitudinal data , expectation–maximization algorithm , computer science , set (abstract data type) , panel data , maximum likelihood , statistics , structural equation modeling , mathematics , data mining , programming language
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.