COMPARING THE EFFICIENCY OF STRUCTURAL AND FUNCTIONAL METHODS IN MEASUREMENT ERROR MODELS

The paper is a survey of recent investigations by various researchers into the relative efficiencies of structural and functional estimators of the regression parameters in a measurement error model. Structural methods, in particular the quasi-score (QS) method, take advantage of the knowledge of the regressor distribution (if available). Functional methods, in particular the corrected score (CS) method, discard such knowledge and work even if such knowledge is not available. Among other results, it has been shown that QS is more efficient than CS as long as the regressor distribution is completely known. However, if nuisance parameters in the regressor distribution are to be estimated, this no more remains true, in general. But by modifying the QS method, the adverse effect of the nuisance parameters can be overcome. For small measurement errors, the efficiencies of QS and CS become almost indistinguishable, whether nuisance parameters are present or not. QS is (asymptotically) biased if the regressor distribution has been misspecified, while CS is always consistent and thus more robust than QS.