BIRS Workshop Lecture Videos
Simultaneous treatment of unspeciﬁed heteroskedastic model error distribution and mismeasured covariates for restricted moment models Garcia, Tanya
This paper is concerned with the consistent and eﬃcient estimationof parameters in general regression models with mismeasured covariates. We assume the distributions of the model error and covariates are completely unspeciﬁed, and that the measurement error distribution is a general parametric distribution with unknown variance-covariance. In this general setting, we construct root-n consistent, asymptotically normal and locally eﬃcient estimators based on the semiparametric eﬃcient score. Constructing the consistent estimator does not involve estimating the unknown distributions, nor modeling the potential model error heteroskedasticity. Instead, a consistent estimator is formed under possibly incorrect working models for the model error distribution, the error-prone covariate distribution, or both. A simulation study demonstrates that our method is robust and performs well for diﬀerent incorrect working models, and various homoskedastic and heteroskedastic regression models with error-prone covariates. The usefulness of the method is further illustrated in a real data example.
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