Ordinal Regression

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

Chapter Chapter 11 in Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling, 2023, pp 241-291 from Springer

Abstract: Abstract Ordinal regression modeling of correlated sets of polytomous outcomes using the cumulative logit link function based on either individual outcomes or cumulative outcomes is addressed allowing for non-constant dispersions. For both of these two types of outcomes, formulations are provided for standard generalized estimating equations (GEE) modeling, for partially modified GEE modeling, for fully modified GEE modeling, and for extended linear mixed modeling (ELMM). These formulations include estimating equations, gradient vectors, and Hessian matrices. Alternate correlation structures and their estimation are also addressed.

Keywords: Correlated polytomous outcomes; Extended linear mixed modeling; Generalized estimating equations; Ordinal regression; Newton’s method; Non-constant dispersions (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-41988-1_11

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DOI: 10.1007/978-3-031-41988-1_11

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