Semiparametric inference in a partial linear model

In a partial linear model, the dependence of a response variate Y on covariates (W, X) is given by Y = W beta + eta(X)+ E, where E is independent of (W, X) with densities g and f, respectively. In this paper an asymptotically efficient estimator of beta is constructed solely under mild smoothness assumptions on the unknown eta, f and g, thereby removing the assumption of finite residual variance on which all least-squares-type estimators available in the literature are based.