Journal
BIOMETRIKA
Volume 88, Issue 4, Pages 973-985Publisher
BIOMETRIKA TRUST
DOI: 10.1093/biomet/88.4.973
Keywords
marginal model; quasi likelihood; random effects; variance estimation
Ask authors/readers for more resources
We investigate the impact of model violations on the estimate of a regression coefficient in a generalised linear mixed model. Specifically, we evaluate the asymptotic relative bias that results from incorrect assumptions regarding the random effects. We compare the impact of model violation for two parameterisations of the regression model. Substantial bias in the conditionally specified regression point estimators can result from using a simple random intercepts model when either the random effects distribution depends on measured covariates or there are autoregressive random effects. A marginally specified regression structure that is estimated using maximum likelihood is much less susceptible to bias resulting from random effects model misspecification.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available