Statistics and Data Science Seminar
Prof. Hua Liang
University of Rochester Medical Center
Semiparametric Regression Models With Measurement Errors
Abstract: We investigated two semi-parametric models, partially linear model and generalized partially linear model, with error-prone covariates. A
correction-for-attenuation method was developed for estimating the parameter of interest in the partially linear model. The resulting
estimator was shown to be consistent and its asymptotic distribution theory has been derived. Consistent standard error estimates using
sandwich-type ideas were also developed. For generalized partially linear model, we proposed estimators of parameter and nonparametric
function by using local linear regression, simulation extrapolation technique, and generalized estimating equation. The asymptotic normality
of the estimators of the parameter, the bias and variance of the estimators of the nonparametric component were derived under appropriate
assumptions. We illustrated the numerical performance of the proposed methods via simulation and examples, discussed the potential topics for
further work.
Tea at 4:05 PM.
Wednesday April 15, 2009 at 4:15 PM in SEO 636