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On the geometry of measurement error models

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  • Paul Marriott

Abstract

The problem of undertaking inference in the classical linear model when the covariates have been measured with error is investigated from a geometric point of view. Under the assumption that the measurement error is small, relative to the total variation in the data, a new model is proposed which has good inferential properties. An inference technique which exploits the geometric structure is shown to be computationally simple, efficient and robust to measurement error. The method proposed is illustrated by simulation studies. Copyright Biometrika Trust 2003, Oxford University Press.

Suggested Citation

  • Paul Marriott, 2003. "On the geometry of measurement error models," Biometrika, Biometrika Trust, vol. 90(3), pages 567-576, September.
    • Handle: RePEc:oup:biomet:v:90:y:2003:i:3:p:567-576
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    Cited by:

    1. K. Anaya-Izquierdo & P. Marriott, 2007. "Local mixtures of the exponential distribution," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 59(1), pages 111-134, March.
    2. Paul Marriott, 2007. "Extending local mixture models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 59(1), pages 95-110, March.

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