Generalized Partially Linear Measurement Error Models

Abstract

This article considers generalized partially linear models when the linear covariate is measured with additive error. We propose estimators of parameter and nonparametric function by using local linear regression, the SIMEX technique, and generalized estimating equation. The asymptotic normality of the estimators of the parameter, and bias and variance of the estimators of the nonparametric component are derived under appropriate assumptions. In addition, the generalization to clustered measurements is discussed. The approaches are used to the analysis of data from the Framingham Heart Study. A simulation experiment is conducted for an illustration.