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Nonlinear Mixed Effects Models for Repeated Measures Data

Lindstrom, Mary J., Bates, Douglas M.·1990·Biometrics

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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Metadata

Authors
Lindstrom, M. J., & Bates, D. M.
Venue
Biometrics
Publisher
JSTOR
Published
1990-09
Volume
46
Issue
3
Pages
673
ISSN
0006-341X (ISSN-L)
OpenAlex
W1993147091
Cited by
1,795
References
1
FWCI
14.90
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Nonlinear Mixed Effects Models for Repeated Measures Data. https://app.cochat.ai/papers/10.2307/2532087
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Lindstrom, M. J., & Bates, D. M. (1990). Nonlinear Mixed Effects Models for Repeated Measures Data. Biometrics. https://doi.org/10.2307/2532087

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