Bias Correction in Generalized Linear Mixed Models with Multiple Components of Dispersion
Bias Correction in Generalized Linear Mixed Models with Multiple Components of Dispersion
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DOI：
10.1080/01621459.1996.10476971
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发表时间：
1996-09
影响因子：
3.7
通讯作者：
Xihong Lin;N. Breslow
中科院分区：
文献类型：
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作者：
Xihong Lin;N. Breslow
本文给出了具有正则连接函数和多组独立随机效应的广义线性混合模型中惩罚拟似然估计的回归系数和方差分量的渐近偏差的一般公式。易于计算的校正矩阵产生的方差分量估计对于较小的方差分量具有令人满意的渐近行为，并且显著减少了较大值的偏差。对于PQL估计的回归系数，建立了一阶和二阶校正程序。通过对一个涉及雄性和雌性交配随机效应的实验的分析，说明了这些方法，并在模拟研究中对它们的性质进行了评估。
Abstract General formulas are derived for the asymptotic bias in regression coefficients and variance components estimated by penalized quasi-likelihood (PQL) in generalized linear mixed models with canonical link function and multiple sets of independent random effects. Easily computed correction matrices result in variance component estimates that have satisfactory asymptotic behavior for small values of the variance components and significantly reduce bias for larger values. Both first-order and second-order correction procedures are developed for regression coefficients estimated by PQL. The methods are illustrated through an analysis of an experiment on salamander matings involving crossed male and female random effects, and their properties are evaluated in a simulation study.