Usable and Precise Asymptotics for Generalized Linear Mixed Model Analysis and Design
推导了广义线性混合模型中用于置信区间和Wald检验的精确渐近结果，通过分析Fisher信息矩阵的领先项行为，得到简单的学生化正态形式，并用于近似局部D最优设计。
Abstract We derive precise asymptotic results that are directly usable for confidence intervals and Wald hypothesis tests for likelihood-based generalized linear mixed model analysis. The essence of our approach is to derive the exact leading term behaviour of the Fisher information matrix when both the number of groups and number of observations within each group diverge. This leads to asymptotic normality results with simple studentizable forms. Similar analyses result in tractable leading term forms for the determination of approximate locally D-optimal designs.