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A Bayesian approach to nonlinear random effects models.

A Racine-Poon

    Biometrics
    |December 1, 1985
    PubMed
    Summary
    This summary is machine-generated.

    This study explores nonlinear random effects models using a Bayesian approach. The analysis method is based on Lindley and Smith (1972) and utilizes a numerical technique related to the EM algorithm.

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    Area of Science:

    • Statistics
    • Statistical Modeling

    Background:

    • Nonlinear random effects models are widely used in various fields.
    • Bayesian inference offers a flexible framework for complex models.

    Purpose of the Study:

    • To analyze nonlinear random effects models using a Bayesian perspective.
    • To adapt and apply established statistical methods to these models.

    Main Methods:

    • The analysis follows the methodology proposed by Lindley and Smith (1972).
    • A numerical method, related to the Expectation-Maximization (EM) algorithm, is employed for computation.

    Main Results:

    • The study demonstrates the application of Bayesian methods to nonlinear random effects models.
    • The numerical approach provides a viable computational strategy.

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    Conclusions:

    • Bayesian analysis is a suitable approach for nonlinear random effects models.
    • The adapted numerical methods enhance the practical application of these models.