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Comparing the efficiency of structural and functional methods in measurement error models


Authors: H. Schneeweiss and A. Kukush
Original publication: Teoriya Imovirnostei ta Matematichna Statistika, tom 80 (2009).
Journal: Theor. Probability and Math. Statist. 80 (2010), 131-142
MSC (2000): Primary 62F12, 62J02
DOI: https://doi.org/10.1090/S0094-9000-2010-00800-3
Published electronically: August 20, 2010
MathSciNet review: 2541958
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Abstract: 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 longer 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.


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Additional Information

H. Schneeweiss
Affiliation: Department of Statistics, University of Munich, Akademiestrasse 1, Munich 80799, Germany
Email: schneew@stat.uni-muenchen.de

A. Kukush
Affiliation: Kyiv National Taras Shevchenko University, Volodymyrska Street, 64, Kyiv 01033, Ukraine
Email: alexander_kukush@univ.kiev.ua

DOI: https://doi.org/10.1090/S0094-9000-2010-00800-3
Keywords: Measurement errors, errors in variables, quasi score, corrected score, structural methods, functional methods, efficiency comparison
Published electronically: August 20, 2010
Article copyright: © Copyright 2010 American Mathematical Society

American Mathematical Society