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Title: Locally efficient semiparametric estimators for a class of Poisson models with error-prone covariates Authors:  Jianxuan Liu - Syracuse University (United States) [presenting]
Yanyuan Ma - Pennsylvania State University (United States)
Abstract: The presence of measurement error may cause bias in parameter estimation and can lead to incorrect conclusions in data analyses. Despite a large body of literature on general measurement error problems, relatively few works exist to handle Poisson models. We thoroughly study Poisson models with errors in covariates and propose consistent and locally efficient semiparametric estimators. The resultant estimators are shown to be root-n consistent, asymptotically normal and locally efficient. We assess the finite sample performance of the estimators through extensive simulation studies and illustrate the proposed methodologies by analyzing data from the Stroke Recovery in Underserved Populations Study.