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Technical note: Estimating parameters of nonlinear segmented models.

J G Fadel1

  • 1Department of Animal Science, University of California, Davis 95616, USA. jgfadel@ucdavis.edu

Journal of Dairy Science
|February 10, 2004
PubMed
Summary
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A new method improves parameter estimation for segmented models by systematically determining starting values, leading to more accurate change point detection and reduced error compared to traditional grid searches.

Area of Science:

  • Statistics
  • Biostatistics
  • Pharmacokinetics

Background:

  • Accurate parameter estimation is crucial for segmented models, especially those with change points or lags.
  • Traditional methods for determining starting values in nonlinear procedures can lead to suboptimal parameter estimates.

Purpose of the Study:

  • To develop an applied technique for estimating parameters in segmented models using SAS PROC NLIN.
  • To improve the selection of starting values for parameters, particularly the change point (lag), to achieve better model fits.

Main Methods:

  • Utilized the Statistical Analysis System's nonlinear procedure (SAS PROC NLIN).
  • Developed a modified technique for determining starting values by systematically fixing the change point (B3) over a range of values.
  • Compared the modified method with a traditional grid search for starting values.

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Main Results:

  • The modified method yielded a lower square root of mean square error (RMSE) compared to the traditional method.
  • The estimated change point (B3) was more accurate with the modified method (3.5) than the traditional method (4.5).

Conclusions:

  • The developed modified technique provides a more robust approach to parameter estimation for segmented models.
  • This method enhances the accuracy of identifying change points or lags in statistical modeling.
  • The technique is effective in SAS PROC NLIN and adaptable to other statistical software.