Misspecified maximum likelihood estimates and generalised linear mixed models

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...

Full description

Saved in:
Bibliographic Details
Published in:Biometrika 2001-12, Vol.88 (4), p.973-985
Main Authors: Heagerty, Patrick J., Kurland, Brenda F.
Format: Article
Language:English
Subjects:
Bias
Coefficients
Estimates
Estimation bias
Estimators
Exact sciences and technology
Linear regression
Marginal model
Mathematical foundations
Mathematics
Maximum likelihood estimation
Maximum likelihood estimators
Probability and statistics
Quasilikelihood
Random effects
Random variables
Regression analysis
Regression coefficients
Sciences and techniques of general use
Standard error
Statistical variance
Statistics
Variance estimation
Violations
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0006-3444
1464-3510
DOI:10.1093/biomet/88.4.973