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Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS by Edward F. Vonesh

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5

Generalized Linear and Nonlinear Mixed-Effects Models

5.1  The generalized linear mixed-effects (GLME) model

5.2  Examples of GLME models

5.3  The generalized nonlinear mixed-effects (GNLME) model

5.4  Examples of GNLME models

5.5  Summary

In the preceding chapter we considered two classes of models for correlated response data, generalized linear models (GLIM's) and generalized nonlinear models (GNLM's). Both classes focus on population-averaged (PA) inference in which model parameters describe various characteristics of an underlying marginal distribution. For example, the vector β of regression parameters is used to predict the average response in a population of individuals and also to describe what effect different covariates have on ...

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