This paper presents the empirical likelihood inferences for a class of varying-coefficient models with error-prone covariates. We focus on the case that the covariance matrix of the measurement errors is unknown and neither repeated measurements nor validation data are available. We propose an...

In this paper, we present a variable selection procedure by combining basis function approximations with SCAD penalty for semiparametric varying coefficient partially linear models. The proposed procedure simultaneously selects significant variables in the parametric components and the...

This paper focuses on the variable selections for semiparametric varying coefficient partially linear models when the covariates in the parametric and nonparametric components are all measured with errors. A bias-corrected variable selection procedure is proposed by combining basis function...