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Flexible Parametric Measurement Error Models

By Raymond J. Carroll, Kathryn Roeder and Larry Wasserman

Abstract

SUMMARY. Inferences in measurement error models can be sensitive to modeling assumptions. Specifically, if the model is incorrect, the estimates can be inconsistent. To reduce sensitivity to modeling assumptions and yet still retain the efficiency of parametric inference, we propose using flexible parametric models that can accommodate departures from standard parametric models. We use mixtures of normals for this purpose. We study two cases in detail: a linear errors-in-variables model and a change-point Berkson model

Topics: KEY WORDS, Berkson model, Change point, Errors-in-variables, Markov chain Monte Carlo, Normal mix
Year: 1999
OAI identifier: oai:CiteSeerX.psu:10.1.1.133.918
Provided by: CiteSeerX
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