Fitting nonlinear and constrained generalized estimating equations with optimization software.

M Contreras; L M Ryan

doi:10.1111/j.0006-341x.2000.01268.x

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Fitting nonlinear and constrained generalized estimating equations with optimization software.

M Contreras·L M Ryan

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Abstract

In this article, we present an estimation approach for solving nonlinear constrained generalized estimating equations that can be implemented using object-oriented software for nonlinear programming, such as nlminb in Splus or fmincon and lsqnonlin in Matlab. We show how standard estimating equation theory includes this method as a special case so that our estimates, when unconstrained, will remain consistent and asymptotically normal. To illustrate this method, we fit a nonlinear dose-response model with nonnegative mixed bound constraints to clustered binary data from a developmental toxicity study. Satisfactory confidence intervals are found using a nonparametric bootstrap method when a common correlation coefficient is assumed for all the dose groups and for some of the dose-specific groups.

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Animals Biometry Confidence Intervals Diethylhexyl Phthalate Female Models, Statistical Pregnancy Software Statistics, Nonparametric Teratogens Toxicity Tests Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.Research Support, U.S. Gov't, P.H.S.

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M Contreras, L M Ryan. 2000. Fitting nonlinear and constrained generalized estimating equations with optimization software.. https://doi.org/10.1111/j.0006-341x.2000.01268.x

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