Flexible Parametric Measurement Error Models
- 1 March 1999
- journal article
- Published by Oxford University Press (OUP) in Biometrics
- Vol. 55 (1), 44-54
- https://doi.org/10.1111/j.0006-341x.1999.00044.x
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.This publication has 17 references indexed in Scilit:
- 10.1007/978-1-4899-4485-6_10Crossref Listing of Deleted Dois, 2000
- Practical Bayesian Density Estimation Using Mixtures of NormalsJournal of the American Statistical Association, 1997
- A Reference Bayesian Test for Nested Hypotheses and its Relationship to the Schwarz CriterionJournal of the American Statistical Association, 1995
- Bayesian Density Estimation and Inference Using MixturesJournal of the American Statistical Association, 1995
- Semiparametric Efficiency in Multivariate Regression Models with Missing DataJournal of the American Statistical Association, 1995
- Markov Chains for Exploring Posterior DistributionsThe Annals of Statistics, 1994
- Bayesian analysis of outlier problems using the Gibbs samplerStatistics and Computing, 1991
- Sampling-Based Approaches to Calculating Marginal DensitiesJournal of the American Statistical Association, 1990
- The Calculation of Posterior Distributions by Data AugmentationJournal of the American Statistical Association, 1987
- A Generalization of Sampling Without Replacement from a Finite UniverseJournal of the American Statistical Association, 1952