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Fitting genetic models with LISREL: hypothesis testing.

M C Neale, A C Heath, J K Hewitt

    Behavior Genetics
    |January 1, 1989
    PubMed
    Summary
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    This study introduces mathematical model fitting, highlighting maximum-likelihood estimates for structural models. Likelihood-ratio statistics offer robust significance testing, particularly for twin data analysis.

    Area of Science:

    • Mathematical Statistics
    • Biometric Modeling
    • Quantitative Genetics

    Background:

    • Model fitting is crucial for understanding complex data structures.
    • Maximum-likelihood estimation provides a powerful framework for parameter estimation.
    • Likelihood-ratio statistics are essential for hypothesis testing in statistical modeling.

    Purpose of the Study:

    • To introduce the mathematical theory of model fitting.
    • To describe properties and advantages of maximum-likelihood estimates in structural modeling.
    • To compare standard errors with likelihood-ratio statistics for parameter estimation.

    Main Methods:

    • Mathematical theory of model fitting.
    • Description of maximum-likelihood estimation properties.

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  • Application of likelihood-ratio (L-R) statistics for model identification and significance testing.

    • Main Results:

    • Maximum-likelihood estimates offer advantages for fitting structural models.
    • Likelihood-ratio tests are invariant to parameter transformation, providing robust significance tests.
    • Guidelines for fitting models to twin data are presented, balancing parsimony and data description.

    Conclusions:

    • Likelihood-ratio statistics are recommended for robust significance testing in structural modeling.
    • The study provides practical guidelines for fitting models to twin data.
    • Balancing model parsimony and data description is key for effective structural modeling.