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Related Experiment Videos

A Newton procedure for conditionally linear mixed-effects models.

Shelley A Blozis1

  • 1Psychology Department, University of California, Davis, California 95616, USA. sablozis@ucdavis.edu

Behavior Research Methods
|January 11, 2008
PubMed
Summary
This summary is machine-generated.

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This study reviews Newton procedures for analyzing mixed-effects models with nonlinearly related parameters. It details a maximum-likelihood estimation method applicable to various models, including factor analysis and latent curve models, using statistical software.

Area of Science:

  • Statistics
  • Quantitative Psychology
  • Biostatistics

Background:

  • Mixed-effects models are widely used in various scientific disciplines.
  • Analyzing mean and covariance structures in these models can be complex, especially when parameters enter nonlinearly.
  • Existing methods may not adequately address the complexities of nonlinear parameter relationships.

Purpose of the Study:

  • To review and present Newton procedures for analyzing mean and covariance structures in conditionally linear mixed-effects models.
  • To provide a framework encompassing hierarchical linear models, linear and nonlinear factor analysis, and nonlinear latent curve models.
  • To describe a full maximum-likelihood estimation procedure and its application using statistical software.

Main Methods:

  • Review of Newton procedures for parameter estimation in mixed-effects models.

Related Experiment Videos

  • Description of a maximum-likelihood estimation framework for models with conditionally linear parameters.
  • Application and illustration using the Mx statistical software package.

    • Main Results:

    • Newton procedures are effective for analyzing mean and covariance structures with nonlinear parameter functions.
    • The described framework accommodates a range of complex statistical models.
    • A practical example with Mx syntax demonstrates the estimation procedure.

    Conclusions:

    • The reviewed Newton procedures offer a robust method for analyzing complex mixed-effects models.
    • The maximum-likelihood estimation approach, implemented in software like Mx, facilitates the analysis of these models.
    • This work provides valuable insights for researchers dealing with nonlinear mixed-effects models.