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Biometrika 1996 83(4):813-824; doi:10.1093/biomet/83.4.813
© 1996 by Biometrika Trust
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Likelihood analysis for errors-in-variables regression with replicate measurements

DANIEL W. SCHAFER and KATHLEEN G. PURDY

Department of Statistics, Oregon State University Corvallis, Oregon 97331-4606, U.S.A.

This paper advocates likelihood analysis for regression models with measurement errors in explanatory variables, for data problems in which the relevant distributions can be adequately modelled. Although computationally difficult, maximum likelihood estimates are more efficient than those based on first and second moment assumptions, and likelihood ratio inferences can be substantially better than those based on asymptotic normality of estimates. The EM algorithm is presented as a straightforward approach for likelihood analysis of normal linear regression with normal explanatory variables, and normal replicate measurements.

Key Words: EM algorithm • Errors-in-variables • Measurement-error-model regression • Structural model


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