Robust inference for sparse cluster-correlated count data

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.
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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

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John J Hanfelt, Yi Pan, Ruosha Li, Pierre Payment. Robust inference for sparse cluster-correlated count data. Journal of Multivariate Analysis, Elsevier, 2011, 102 (1), pp.182-192. ⟨10.1016/j.jmva.2010.09.003⟩. ⟨pasteur-00819404⟩

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