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Generalized Linear Mixed Models

Charles E. McCulloch, John Neuhaus

Open publisher page 135 citations

Abstract

Abstract Generalized linear mixed models (GLMMs) are a class of models that incorporates random effects into the linear predictor of a generalized linear model (GLM). This allows the modeling of correlated data within the context of GLMs and greatly extends their breadth of applicability. They thus include both linear mixed models (LMMs) and GLMs as special cases.

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What this paper is about

Abstract Generalized linear mixed models (GLMMs) are a class of models that incorporates random effects into the linear predictor of a generalized linear model (GLM). This allows the modeling of correlated data within the context of GLMs and greatly extends their breadth of applicability. They thus include both linear mixed models (LMMs) and GLMs as special cases.

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OpenAlex reports 135 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Abstract Generalized linear mixed models (GLMMs) are a class of models that incorporates random effects into the linear predictor of a generalized linear model (GLM). This allows the modeling of correlated data within the context of GLMs and greatly extends their breadth of applicability. They thus include both linear mixed models (LMMs) and GLMs as special cases.

Key concepts: Generalized linear mixed model, Generalized linear model, Hierarchical generalized linear model, Generalized linear array model, Linear model, Applied mathematics, Generalized additive model, Context (archaeology)

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