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Estimation of the Nonlinear Random Coefficient Model when Some Random Effects Are Separable

Abstract

A method is presented for marginal maximum likelihood estimation of the nonlinear random coefficient model when the response function has some linear parameters. This is done by writing the marginal distribution of the repeated measures as a conditional distribution of the response given the nonlinear random effects. The resulting distribution then requires an integral equation that is of dimension equal to the number of nonlinear terms. For nonlinear functions that have linear coefficients, the improvement in computational speed and accuracy using the new algorithm can be dramatic. An illustration of the method with repeated measures data from a learning experiment is presented.

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Correspondence to Robert Cudeck.

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du Toit, S.H.C., Cudeck, R. Estimation of the Nonlinear Random Coefficient Model when Some Random Effects Are Separable. Psychometrika 74, 65 (2009). https://doi.org/10.1007/s11336-008-9107-7

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Keywords

  • random coefficient model
  • nonlinear regression
  • subject-specific model
  • repeated measures data
  • numerical integration