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Title: Usable and Precise Asymptotics for Generalized Linear Mixed Model Analysis and Design
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.

 
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Award ID(s):
1934568
NSF-PAR ID:
10398625
Author(s) / Creator(s):
; ;
Publisher / Repository:
Oxford University Press
Date Published:
Journal Name:
Journal of the Royal Statistical Society Series B: Statistical Methodology
Volume:
84
Issue:
1
ISSN:
1369-7412
Format(s):
Medium: X Size: p. 55-82
Size(s):
["p. 55-82"]
Sponsoring Org:
National Science Foundation
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