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Nonlinear Mixed Effects Models for Repeated Measures Data

Mary J. Lindstrom and Douglas M. Bates
Biometrics
Vol. 46, No. 3 (Sep., 1990), pp. 673-687
DOI: 10.2307/2532087
Stable URL: http://www.jstor.org/stable/2532087
Page Count: 15
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Nonlinear Mixed Effects Models for Repeated Measures Data
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Abstract

We propose a general, nonlinear mixed effects model for repeated measures data and define estimators for its parameters. The proposed estimators are a natural combination of least squares estimators for nonlinear fixed effects models and maximum likelihood (or restricted maximum likelihood) estimators for linear mixed effects models. We implement Newton-Raphson estimation using previously developed computational methods for nonlinear fixed effects models and for linear mixed effects models. Two examples are presented and the connections between this work and recent work on generalized linear mixed effects models are discussed.

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