Misspecified maximum likelihood estimates and generalised linear mixed models
Misspecified maximum likelihood estimates and generalised linear mixed models
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DOI：
10.1093/biomet/88.4.973
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发表时间：
2001-12-01
期刊：
影响因子：
2.7
通讯作者：
Kurland, BF
中科院分区：
文献类型：
--
作者：
Heagerty, PJ;Kurland, BF
我们研究了广义线性混合模型中模型违规对回归系数估计的影响。具体来说，我们评估的渐近相对偏差，结果从不正确的假设有关的随机效应。我们比较了两个参数化的回归模型的模型违规的影响。当随机效应分布取决于测量的协变量或存在自回归随机效应时，使用简单随机截距模型可能会导致条件指定回归点估计量的实质性偏倚。使用最大似然估计的边际指定回归结构对随机效应模型误指定导致的偏倚的敏感性要小得多。
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.