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Parameter estimation for nonlinear models with emphasis on compartmental models.

D M Allen

    Biometrics
    |September 1, 1983
    PubMed
    Summary
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    New computational techniques simplify parameter estimation in nonlinear models, reducing programming effort and enabling more flexible variance-covariance structures. These methods are especially useful for analyzing stochastic compartmental models.

    Area of Science:

    • Computational statistics
    • Mathematical modeling

    Background:

    • Estimating parameters in nonlinear models can be complex and programming-intensive.
    • Existing methods may limit the flexibility of variance-covariance structures.
    • Analysis of stochastic compartmental models presents unique challenges.

    Purpose of the Study:

    • To present novel techniques for parameter estimation in nonlinear models.
    • To reduce programming effort for complex model analyses.
    • To facilitate inference on implicit functions and allow general variance-covariance structures.

    Main Methods:

    • Development of computational techniques for nonlinear model parameter estimation.
    • Implementation strategies for computer programs.
    • Application to stochastic compartmental models.

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    Main Results:

    • Reduced programming effort for implementing parameter estimation.
    • Enhanced ability to perform inference on implicit functions of parameters.
    • Support for more general variance-covariance structures in analyses.

    Conclusions:

    • The described techniques offer a more efficient and flexible approach to parameter estimation in nonlinear models.
    • These methods are particularly advantageous for the analysis of stochastic compartmental models.
    • The techniques streamline complex statistical analyses and improve inferential capabilities.