Flexible parametric measurement error models
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Updated: Oct 8, 2026

Precision Measurements and Parametric Models of Vertebral Endplates
Published on: September 17, 2019
R J Carroll1, K Roeder, L Wasserman
1Department of Statistics, Texas A&M University, College Station 77843-3143, USA.
Abstract:
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.
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