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Measurement error model for misclassified binary responses
Author(s) -
Roy Surupa,
Banerjee T.,
Maiti Tapabrata
Publication year - 2005
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1886
Subject(s) - covariate , unobservable , observational error , computer science , statistics , errors in variables models , regression analysis , sensitivity (control systems) , binary number , regression , binary data , econometrics , mathematics , arithmetic , electronic engineering , engineering
The article considers regression models for binary response in a situation when the response is subject to classification error. It is also assumed that some of the covariates are unobservable, but measurements on its surrogates are available. Likelihood based analysis is developed to fit the model. A sensitivity analysis is also carried out through simulation to ascertain the effect of ignoring classification error and/or measurement error on the estimation of regression parameters. At the end, the methodology developed in this paper is illustrated through an example. Copyright © 2004 John Wiley & Sons, Ltd.