Abstract
A brief introduction to the mathematical theory involved in model fitting is provided. The properties of maximum-likelihood estimates are described, and their advantages in fitting structural models are given. Identification of models is considered. Standard errors of parameter estimates are compared with the use of likelihood-ratio (L-R) statistics. For structural modeling, L-R tests are invariant to parameter transformation and give robust tests of significance. Some guidelines for fitting models to data collected from twins are given, with discussion of the relative merits of parsimony and data description.
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The theoretical work and data analysis described in this paper were made possible by NATO Grant 86/0823 and grants from the Belgian National Research Fund, the State University of Gent, and the Catholic University of Leuven. We are also grateful to Drs. R. Vlietinck and R. Derom for excellent organization of the successful workshop. A.C.H., M.C.N., J.K.H., and L.J.E. were supported by NIH Grants MH-40828, AA-06781, GM-30250, AG-04954, and HL-31010. D.W.F. was supported by NIH Grants HD-19802 and HD-18426.
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Neale, M.C., Heath, A.C., Hewitt, J.K. et al. Fitting genetic models with LISREL: Hypothesis testing. Behav Genet 19, 37–49 (1989). https://doi.org/10.1007/BF01065882
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DOI: https://doi.org/10.1007/BF01065882