Robust inference for sparse cluster-correlated count data

John J Hanfelt; Yi Pan; Ruosha Li; Pierre Payment

doi:10.1016/j.jmva.2010.09.003

Journal articles

Robust inference for sparse cluster-correlated count data

John J Hanfelt ^{1, *} Yi Pan ¹ Ruosha Li ¹ Pierre Payment ²

* Corresponding author

1 Department of Biostatistics & Bioinformatics

2 INRS-IAF - Institut Armand Frappier

Abstract : Standard methods for the analysis of cluster-correlated count data fail to yield valid inferences when the study is finely stratified and the interest is in assessing the intracluster correlation structure. We present an approach, based upon exactly adjusting an estimating function for the bias induced by the fitting of stratum-specific effects, that requires modeling only the first two joint moments of the observations and that yields consistent and asymptotically normal estimators of the correlation parameters.

Keywords : Profile estimating function Plug-in bias Poisson overdispersion Generalized estimating equation Household aggregation Intracluster correlation Nuisance parameters Small geographic area

Document type :

Journal articles

Domain :

Life Sciences [q-bio] / Biotechnology

Computer Science [cs] / Biotechnology

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https://hal-riip.archives-ouvertes.fr/pasteur-00819404
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Submitted on : Wednesday, May 1, 2013 - 3:39:50 AM
Last modification on : Friday, September 20, 2019 - 9:45:17 AM

Robust inference for sparse cluster-correlated count data

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Robust inference for sparse cluster-correlated count data

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