Error variance estimation for the partially linear model

Abstract

To address the <i>curse of dimensionality</i> problem associated with multivariate nonparametric regression models, we consider partially linear regression models. A contribution to statistical inference in the partially linear regression model is to propose estimators of the error variance with good asymptotic properties. We propose two estimators of the error variance in a partially linear regression model and their respective asymptotic normality. Using a simulation–based study, we will compare the performance of our estimator with the performance of other concurrent estimators existing in the statistical literature