Flexible parametric measurement error models

Inferences in measurement error models can be sensitive to modeling assumpt ions. Specifically, if the model is incorrect, the estimates can be inconsi stent. To reduce sensitivity to modeling assumptions and yet still retain t he 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.