Efficient Estimation in Single-Index Regression

Abstract

Semiparametric single-index regression involves an unknown finite dimensional parameter and an unknown (link) function. We consider estimation of the parameter via the pseudo maximum likelihood method. For this purpose we estimate the conditional density of the response given a candidate index and maximize the obtained likelihood. We show that this technique of adaptation yields an asymptotically efficient estimator : it has minimal variance among all estimators.