Instrumental variable approach to covariate measurement error in generalized linear models
نویسندگان
چکیده
The paper presents the method of moments estimation for generalized linear measurement error models using the instrumental variable approach. The measurement error has a parametric distribution that is not necessarily normal, while the distributions of the unobserved covariates are nonparametric. We also propose simulation-based estimators for the situation where the closed forms of the moments are not available. The proposed estimators are strongly consistent and asymptotically normally distributed under some regularity conditions. Finite sample performances of the estimators are investigated through simulation studies.
منابع مشابه
Maximum likelihood estimation of generalized linear models with covariate measurement error
Generalized linear models with covariate measurement error can be estimated by maximum likelihood using gllamm, a program that fits a large class of multilevel latent variable models (Rabe-Hesketh, Skrondal, and Pickles 2004b). The program uses adaptive quadrature to evaluate the log-likelihood, producing more reliable results than many other methods (Rabe-Hesketh, Skrondal, and Pickles 2002). ...
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