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- W2076262123 abstract "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." @default.
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- W2076262123 date "2005-01-01" @default.
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- W2076262123 title "Measurement error model for misclassified binary responses" @default.
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