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Robust inference for sparse cluster-correlated count data

Title: Robust inference for sparse cluster-correlated count data
Authors: Hanfelt, John J; Pan, Yi; Li, Ruosha; Payment, Pierre
Contributors: Department of Biostatistics & Bioinformatics; Emory University Atlanta, GA; Armand-Frappier Santé Biotechnologie Research Centre (INRS-AFSB); Institut National de la Recherche Scientifique Québec (INRS)-Pasteur Network (Réseau International des Instituts Pasteur)
Source: ISSN: 0047-259X.
Publisher Information: CCSD; Elsevier
Publication Year: 2011
Collection: Réseau International des Instituts Pasteur, Paris: HAL-RIIP
Subject Terms: Generalized estimating equation; Household aggregation; Intracluster correlation; Nuisance parameters; Poisson overdispersion; Plug-in bias; Profile estimating function; Small geographic area; [SDV.BIO]Life Sciences [q-bio]/Biotechnology; [INFO.INFO-BT]Computer Science [cs]/Biotechnology
Description: 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.
Document Type: article in journal/newspaper
Language: English
DOI: 10.1016/j.jmva.2010.09.003
Availability: https://riip.hal.science/pasteur-00819404; https://doi.org/10.1016/j.jmva.2010.09.003
Accession Number: edsbas.C43B13AD
Database: BASE