CONDITIONAL LIKELIHOOD INFERENCE IN GENERALIZED LINEAR MIXED MODELS
CONDITIONAL LIKELIHOOD INFERENCE IN GENERALIZED LINEAR MIXED MODELS
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DOI：
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发表时间：
2004-04
影响因子：
1.4
通讯作者：
N. Sartori;T. Severini
中科院分区：
文献类型：
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作者：
N. Sartori;T. Severini
考虑一个具有典型联系函数的广义线性模型，其中包含固定效应和随机效应.在本文中，我们考虑基于条件似然函数的固定效应的推断。它表明，这个条件似然函数是有效的任何分布的随机效应，因此，由此产生的推断固定效应是不敏感的随机效应分布的误规格。将基于条件似然的推断与基于混合效应模型的似然函数的推断进行比较。
Consider a generalized linear model with a canonical link function, con- taining both fixed and random effects. In this paper, we consider inference about the fixed effects based on a conditional likelihood function. It is shown that this conditional likelihood function is valid for any distribution of the random effects and, hence, the resulting inferences about the fixed effects are insensitive to mis- specification of the random effects distribution. Inferences based on the conditional likelihood are compared to those based on the likelihood function of the mixed ef- fects model.