Locally efficient semiparametric estimators for a class of Poisson models with measurement error. Issue 2 (23rd January 2019)

• Record Type: Journal Article
• Authors: Liu, Jianxuan; Ma, Yanyuan
• Is Part Of: Canadian journal of statistics. Volume 47:Issue 2(2019)
• Page Start: 157
• Page End: 181
• Publication Date: 2019-01-23
• Subjects: Complete and sufficient statistic -- error‐in‐variables -- local efficiency -- Poisson models -- regression calibration -- semiparametric methods; Mathematical statistics -- Periodicals; 519.5
• DOI: 10.1002/cjs.11483
• Languages: English
• ISSNs: 0319-5724

Abstract

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. In this article we thoroughly study Poisson models with errors in covariates and propose consistent and locally efficient semiparametric estimators. 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. The Canadian Journal of Statistics 47: 157–181; 2019 © 2019 Statistical Society of Canada