Nonparametric Estimation of Regression Parameters in Measurement Error Models

Abstrakt

This paper develops the theory of rank estimation for the regression parameters in measurement error models. Using the standard linear rank statistics, R-estimators are defined and their asymptotic properties are studied as robust alternatives to least squares estimators. This paper fills the gap of rank theory in the estimation of parameters of measurement error models. Some simulation results are presented to show the effectiveness of the R-estimators.