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Biometrika 1993 80(4):899-904; doi:10.1093/biomet/80.4.899
© 1993 by Biometrika Trust
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Likelihood analysis for probit regression with measurement errors

D. W. SCHAFER

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

Likelihood analysis is proposed for a probit regression model when one of the explanatory variables is measured with error and replicate measurements of that variable are available on some of the subjects. The distribution of the measurement error and of the unknown explanatory variable, conditional on the known variables, are both taken to be normal. The maximum likelihood estimates can be computed exactly with the EM algorithm. Likelihood ratio tests and confidence intervals can be computed with a Laplace approximation.

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


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