Second term improvement to generalized linear mixed model asymptotics
该文在Jiang等人(2022)关于广义线性混合模型渐近方差收敛速率的基础上，推导了更难估计参数的二阶项显式形式，有助于提高统计推断精度和实验设计效率。
Abstract A recent article by Jiang et al. (2022) on generalized linear mixed model asymptotics derived the rates of convergence for the asymptotic variances of maximum likelihood estimators. If m denotes the number of groups and n is the average within-group sample size then the asymptotic variances have orders m−1 and (mn)−1, depending on the parameter. We extend this theory to provide explicit forms of the (mn)−1 second terms of the asymptotically harder-to-estimate parameters. Improved accuracy of statistical inference and planning are consequences of our theory.