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

Jianxuan Liu, Yanyuan Ma

Research output: Contribution to journalArticle

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 00: 000–000; 2019

Original languageEnglish (US)
JournalCanadian Journal of Statistics
DOIs
StateAccepted/In press - Jan 1 2019

Fingerprint

Poisson Model
Measurement Error
Estimator
Stroke
Parameter Estimation
Covariates
Recovery
Simulation Study
Statistics
Methodology
Class
Measurement error
Semiparametric estimators
Poisson model
Simulation study
Parameter estimation
Finite sample

Keywords

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

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

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