Multinomial Regression

George J. Knafl ()
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George J. Knafl: University of North Carolina at Chapel Hill, School of Nursing

Chapter Chapter 10 in Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling, 2026, pp 269-295 from Springer

Abstract: Abstract 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.

Keywords: Correlated polytomous outcomes; Extended linear mixed modeling; Generalized estimating equations; Multinomial regression; Newton's method; Non-constant dispersions (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-00989-0_10

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DOI: 10.1007/978-3-032-00989-0_10

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