A note on the sensitivity to assumptions of a generalized linear mixed model
成果类型：
Article
署名作者：
Cox, D. R.; Wong, M. Y.
署名单位：
University of Oxford; Hong Kong University of Science & Technology
刊物名称：
BIOMETRIKA
ISSN/ISSBN：
0006-3444
DOI：
10.1093/biomet/asp083
发表日期：
2010
页码：
209214
关键词：
摘要：
A simple case of Poisson regression is used to study the potential gain in efficiency from using a mixed model representation. Possible systematic errors arising from misspecification of the random terms in the model are examined. It is shown in particular that for a special but realistic problem, appreciable bias may arise from misspecification of a random component.