Locally efficient semiparametric estimators for a class of Poisson models with measurement error

Jianxuan Liu, Yanyuan Ma

Research output: Contribution to journalArticle

1 Scopus citations

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

Original languageEnglish (US)
Pages (from-to)157-181
Number of pages25
JournalCanadian Journal of Statistics
Volume47
Issue number2
DOIs
StatePublished - Jun 2019

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Keywords

  • Complete and sufficient statistic
  • Poisson models
  • error-in-variables
  • local efficiency
  • regression calibration
  • semiparametric methods

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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