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Biometrika 2003 90(3):567-576; doi:10.1093/biomet/90.3.567
© 2003 by Biometrika Trust
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On the geometry of measurement error models

Paul Marriott1

1 Department of Statistics & Applied Probability, National University of Singapore, 3 Science Drive 2, Singapore 117543stapkm{at}nus.edu.sg

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.

Key Words: Identification; Laplace method; Latent variable; Linear model; Measurement error; Mixture model; Statistical manifold

Received June 2001. Revised October 2002


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