Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/31008
Authors: 
Schneeweiss, Hans
Augustin, Thomas
Year of Publication: 
2005
Series/Report no.: 
Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 452
Abstract: 
A measurement error model is a regression model with (substantial) measurement errors in the variables. Disregarding these measurement errors in estimating the regression parameters results in asymptotically biased estimators. Several methods have been proposed to eliminate, or at least to reduce, this bias, and the relative efficiency and robustness of these methods have been compared. The paper gives an account of these endeavors. In another context, when data are of a categorical nature, classification errors play a similar role as measurement errors in continuous data. The paper also reviews some recent advances in this field.
Subjects: 
Measurement errors
error in variables
misclassification
efficiency comparison
survival analysis
JEL: 
C13
C20
C24
C25
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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