MAXIMUM POSTERIOR ESTIMATION OF RANDOM EFFECTS IN GENERALIZED LINEAR MIXED MODELS
MAXIMUM POSTERIOR ESTIMATION OF RANDOM EFFECTS IN GENERALIZED LINEAR MIXED MODELS
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发表时间：
2001
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通讯作者：
Jiming Jiang;Hao Jia;Hegang Chen
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作者：
Jiming Jiang;Hao Jia;Hegang Chen
给定观测向量和离散参数向量（方差分量），广义线性混合模型中的固定效应和随机效应通过最大化后验密度来估计。尽管固定效应和随机效应的这种估计取决于方差分量的（未知）向量，但我们从数值和理论上证明，在某些大样本情况下，这些估计的受限版本的一致性不受计算它们的方差分量的影响。该方法适用于使用抽样调查数据进行小面积估计的问题。
Given a vector of observations and a vector of dispersion parameters (variance components), the fixed and random effects in a generalized linear mixed model are estimated by maximizing the posterior density. Although such estimates of the fixed and random effects depend on the (unknown) vector of variance com- ponents, we demonstrate both numerically and theoretically that in certain large sample situations the consistency of a restricted version of these estimates is not af- fected by variance components at which they are computed. The method is applied to a problem of small area estimation using data from a sample survey.