Some Recent Advances in Measurement Error Models and Methods

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.