论文标题
精确广义线性混合模型渐近学的分散参数扩展
Dispersion Parameter Extension of Precise Generalized Linear Mixed Model Asymptotics
论文作者
论文摘要
我们扩展了最近建立的渐近正态性定理，以包含分散参数。新结果表明，所有模型参数的最大似然估计量均具有渐近正常分布，并且在固定效应，随机效应协方差和分散参数之间具有渐近互独立性。分散参数最大似然估计器具有特别简单的渐近分布，可直接基于有效的可能性推断。
We extend a recently established asymptotic normality theorem for generalized linear mixed models to include the dispersion parameter. The new results show that the maximum likelihood estimators of all model parameters have asymptotically normal distributions with asymptotic mutual independence between fixed effects, random effects covariance and dispersion parameters. The dispersion parameter maximum likelihood estimator has a particularly simple asymptotic distribution which enables straightforward valid likelihood-based inference.
