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A simulation study of estimators for generalized linear measurement error models

Zhao, Yuejen and Lee, Andy H. (1996). A simulation study of estimators for generalized linear measurement error models. Journal of Statistical Computation and Simulation,54(1-3):55-74.

Document type: Journal Article
Citation counts: Scopus Citation Count Cited 5 times in Scopus Article | Citations

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Title A simulation study of estimators for generalized linear measurement error models
Author Zhao, Yuejen
Lee, Andy H.
Journal Name Journal of Statistical Computation and Simulation
Publication Date 1996
Volume Number 54
Issue Number 1-3
ISSN 0094-9655   (check CDU catalogue open catalogue search in new window)
Scopus ID 2-s2.0-0030513891
Start Page 55
End Page 74
Total Pages 20
Place of Publication United Kingdom
Publisher Taylor & Francis
Abstract The purpose of this paper is to examine the properties of several bias-corrected estimators for generalized linear measurement error models, along with the naive estimator, in some special settings. In particular, we consider logistic regression, poisson regression and exponential-gamma models where the covariates are subject to measurement error. Monte Carlo experiments are conducted to compare the relative performance of the estimators in terms of several criteria. The results indicate that the naive estimator of slope is biased towards zero by a factor increasing with the magnitude of slope and measurement error as well as the sample size. It is found that none of the biased-corrected estimators always outperforms the others, and that their small sample properties typically depend on the underlying model assumptions.
Keywords Errors-in-variables
Generalized linear models
Monte Carlo experiment
DOI http://dx.doi.org/10.1080/00949659608811720   (check subscription with CDU E-Gateway service for CDU Staff and Students  check subscription with CDU E-Gateway in new window)
 
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Created: Fri, 29 Aug 2014, 20:16:33 CST by Anthony Hornby