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Some recent advances in measurement error models and methods

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Summary

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.

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Authors and Affiliations

  1. Department of Statistics, Ludwig-Maximilians-Universität, Akademiestr. 1, I 80799, München, Germany

    Hans Schneeweiss

  2. Department of Statistics, Ludwig-Maximilians-Universität, Ludwigstr. 33, II 80539, München, Germany

    Thomas Augustin

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  1. Hans Schneeweiss
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  2. Thomas Augustin
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Additional information

This work was supported by the Deutsche Forschungsgemeinschaft (DFG) within the frame of the Sonderforschungsbereich SFB 386. We thank two anonymous referees for their helpful comments.

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Schneeweiss, H., Augustin, T. Some recent advances in measurement error models and methods. Allgemeines Statistisches Arch 90, 183–197 (2006). https://doi.org/10.1007/s10182-006-0229-x

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  • DOI: https://doi.org/10.1007/s10182-006-0229-x

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