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Second term improvement to generalized linear mixed model asymptotics

Author

Listed:
  • Luca Maestrini
  • Aishwarya Bhaskaran
  • Matt P Wand

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.

Suggested Citation

  • Luca Maestrini & Aishwarya Bhaskaran & Matt P Wand, 2024. "Second term improvement to generalized linear mixed model asymptotics," Biometrika, Biometrika Trust, vol. 111(3), pages 1077-1084.
    • Handle: RePEc:oup:biomet:v:111:y:2024:i:3:p:1077-1084.
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