GENERALIZED LINEAR MIXED MODELS - A PSEUDO-LIKELIHOOD APPROACH
GENERALIZED LINEAR MIXED MODELS - A PSEUDO-LIKELIHOOD APPROACH
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DOI：
10.1080/00949659308811554
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发表时间：
1993-01-01
影响因子：
1.2
通讯作者：
OCONNELL, M
中科院分区：
文献类型：
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作者：
WOLFINGER, R;OCONNELL, M
广义线性模型的一个有用的扩展涉及添加随机效应和/或相关误差。一个伪似然估计程序开发，以适应这一类的混合模型的基础上的近似边际模型的平均响应。该过程是通过迭代拟合加权高斯线性混合模型的修改后的因变量。该方法允许灵活的规格的随机效应和相关误差的协方差结构。潜在的指数族分布的额外的分散参数的估计可选地是自动的。该方法考虑到特定对象和总体平均推断，McCullagh和Nelder（1989）的Salamander数据示例用于说明两者。
A useful extension of the generalized linear model involves the addition of random effects and/or correlated errors. A pseudo-likelihood estimation procedure is developed to fit this class of mixed models based on an approximate marginal model for the mean response. The procedure is implemented via iterated fitting of a weighted Gaussian linear mixed model to a modified dependent variable. The approach allows for flexible specification of covariance structures for both the random effects and the correlated errors. An estimate of an additional dispersion parameter for underlying exponential family distributions is optionally automatic. The method allows for subject-specific and population-averaged inference, and the Salamander data example from McCullagh and Nelder (1989) is used to illustrate both.