Estimation of the Nonlinear Random Coefficient Model when Some Random Effects Are Separable |
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Authors: | Stephen H C du Toit Robert Cudeck |
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Institution: | (1) Scientific Software International, Inc., Lincolnwood, USA;(2) Psychology Department, Ohio State University, 240K Lazenby Hall, Columbus, OH 43210, USA |
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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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Keywords: | random coefficient model nonlinear regression subject-specific model repeated measures data numerical integration |
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