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Flexible Parametric Measurement Error Models
Author(s) -
Carroll Raymond J.,
Roeder Kathryn,
Wasserman Larry
Publication year - 1999
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.1999.00044.x
Subject(s) - parametric statistics , parametric model , sensitivity (control systems) , computer science , inference , errors in variables models , observational error , point (geometry) , semiparametric model , econometrics , statistics , mathematics , machine learning , artificial intelligence , geometry , electronic engineering , engineering
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.