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A large class of models derived from generalized linear models
Author(s) -
Nelder John A.
Publication year - 1998
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19981215)17:23<2747::aid-sim40>3.0.co;2-i
Subject(s) - generalized linear model , hierarchical generalized linear model , generalized additive model , class (philosophy) , linear model , generalized linear mixed model , extension (predicate logic) , mathematics , marginal model , random effects model , generalized linear array model , computer science , generalized estimating equation , statistics , regression analysis , artificial intelligence , medicine , meta analysis , programming language
Generalized linear models may be extended in several ways. This paper describes five such extensions: (i) generalized additive models; (ii) the use of quasi‐likelihood; (iii) joint modelling of mean and dispersion; (iv) introduction of extra random components to give hierarchical generalized linear models; (v) modelling of correlated responses within subjects in longitudinal models. These extensions are largely independent, and so can be combined in many ways to produce a large class of models. Finally, a further extension to dynamic forms of the models is sketched. © 1998 John Wiley & Sons, Ltd.