We propose a method of inference for generalized linear mixed models
(GLMM) that in many ways resembles the method of least squares. We also show
that adequate inference about GLMM can be made based on the conditional
likelihood on a subset of the random effects. One of the important features of
our methods is that they rely on weak distributional assumptions about the
random effects. The methods proposed are also computationally feasible.
Asymptotic behavior of the estimates is investigated. In particular,
consistency is proved under reasonable conditions.
Publié le : 1999-12-14
Classification:
Asymptotic properties of estimates,
maximum conditional likelihood,
penalized generalized WLS,
semiparametric inference,
62J12,
62F12
@article{1017939247,
author = {Jiang, Jiming},
title = {Conditional inference about generalized linear mixed
models},
journal = {Ann. Statist.},
volume = {27},
number = {4},
year = {1999},
pages = { 1974-2007},
language = {en},
url = {http://dml.mathdoc.fr/item/1017939247}
}
Jiang, Jiming. Conditional inference about generalized linear mixed
models. Ann. Statist., Tome 27 (1999) no. 4, pp. 1974-2007. http://gdmltest.u-ga.fr/item/1017939247/