Semiparametric additive regression

Abstract

A simple estimator for β is proposed for the model y=x'β+g(1)+error, g smooth but unknown. The approach is to approximate the estimating equation obtained from a semiparametric likelihood and in the simplest case reduces to minimizing the distance between the pseudoresiduals y-x'β and a local linear cross-validated estimate of them. When the errors are independent with finite variance, the bias and variance of the estimate are computed and compared against the least squares estimate with g known