Regression Estimation with Errors in the Variables via the Laplace Transform

Abstract

This paper considers nonparametric regression estimation with errors in the variables. It is a standard assumption that the characteristic function of the covariate error does not vanish on the real line. This assumption is rather strong. In this paper, we assume the covariate error distribution is a convolution of uniform distributions, the characteristic function of which contains zeros on the real line. Our regression estimator is constructed via the Laplace transform. We prove its strong consistency and show its convergence rate. It turns out that zeros in the characteristic function have no effect on the convergence rate of our estimator.

Keywords

• nonparametric regression
• errors in variables
• Laplace transform
• strong consistency
• convergence rate