Generalized linear models for ordered categorical data
Journal article, 2021

Categorical scale data are only ordinal and defined on a finite set. Continuous scale data are only ordinal and defined on a bounded interval. Due to that character, the statistical methods for scale data ought to be based on orders between outcomes only and not any metric involving distance measure. For simple two-sample scale data, variants of classical rank methods are suitable. For regression type of problems, there are known good generalized linear models for separate categories for a long time. In the present article is suggested a new generalized linear type of model based on non parametric statistics for the whole scale. Asymptotic normality for those statistics is also shown and illustrated. Both fixed and random effects are considered.

Generalized linear model

scale data

rank methods

Author

Sture Holm

University of Gothenburg

Chalmers, Mathematical Sciences

Communications in Statistics - Theory and Methods

0361-0926 (ISSN) 1532-415X (eISSN)

Vol. In Press

Subject Categories

Bioinformatics (Computational Biology)

Geophysics

Probability Theory and Statistics

DOI

10.1080/03610926.2021.1921210

More information

Latest update

6/2/2021 7