title: Flexible parametric measurement error models.
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.
authors.affiliation: Department of Statistics, Texas A&M University, College Station 77843-3143, USA.
authors.forename: R J
authors.initials: RJ
authors.lastname: Carroll
authors.forename: K
authors.initials: K
authors.lastname: Roeder
authors.forename: L
authors.initials: L
authors.lastname: Wasserman
mesh_terms: D001699:Biometry; D001991:Bronchitis / Q000209:etiology; D015930:Diet Records; D004391:Dust / Q000009:adverse effects / Q000032:analysis; D006801:Humans; D016014:Linear Models; D015233:Models, Statistical; D012680:Sensitivity and Specificity
chemical_list: D004391:Dust