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Multinomial regression modeling of correlated sets of polytomous outcomes using the generalized logits link function is addressed allowing for non-constant dispersions. Formulations are provided for standard generalized estimating equations (GEE) modeling, partially modified GEE modeling, fully modified GEE modeling, and extended linear mixed modeling (ELMM). These formulations include estimating equations, gradient vectors, and Hessian matrices. Alternate directly specified correlation structures and their estimation are also addressed.

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Multinomial Regression

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  • 发表日期：

    2026年

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  • George J. Knafl

摘要

Multinomial regression modeling of correlated sets of polytomous outcomes using the generalized logits link function is addressed allowing for non-constant dispersions. Formulations are provided for standard generalized estimating equations (GEE) modeling, partially modified GEE modeling, fully modified GEE modeling, and extended linear mixed modeling (ELMM). These formulations include estimating equations, gradient vectors, and Hessian matrices. Alternate directly specified correlation structures and their estimation are also addressed.