Properties of h-Likelihood Estimators in Clustered Data

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We study properties of the maximum h-likelihood estimators for random effects in clustered data. To define optimality in random effects predictions, several foundational concepts of statistics such as likelihood, unbiasedness, consistency, confidence distribution and the Cramer-Rao lower bound are extended. Exact probability statements about interval estimators for random effects can be made asymptotically without a prior assumption. Using the binary-matched pair example, we illustrated that the use of random effects recover information, leading to the boon on estimating treatment effects.
Publisher
WILEY
Issue Date
2020-08
Language
English
Article Type
Article
Citation

INTERNATIONAL STATISTICAL REVIEW, v.88, no.2, pp.380 - 395

ISSN
0306-7734
DOI
10.1111/insr.12354
URI
http://hdl.handle.net/10203/281960
Appears in Collection
RIMS Journal Papers
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