Estimation by data augmentation in regression models with continuous and discrete covariates measured with error.
Estimation methods are considered for regression models which have both misclassified discrete covariates and continuous covariates measured with error. Adjusted parameter estimates are obtained using the method of data augmentation, where the true values of the covariates measured with error are regarded as missing data. Validation data on the covariates are assumed to be available. The distinction between internal and external validation data is emphasized, and its effects on the analysis are examined. The method is illustrated with simulated data.
Kuha
J
J
Nuffield College, Oxford, U.K.
D000465:Algorithms; D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D016013:Likelihood Functions; D015233:Models, Statistical; D012044:Regression Analysis