Second term improvement to generalized linear mixed model asymptotics
成果类型：
Article; Early Access
署名作者：
Maestrini, Luca; Bhaskaran, Aishwarya; Wand, Matt P.
署名单位：
Australian National University; University of Technology Sydney
刊物名称：
BIOMETRIKA
ISSN/ISSBN：
0006-3444
DOI：
10.1093/biomet/asad072
发表日期：
2024
关键词：
摘要：
A recent article by 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.