Robust Estimates in Generalized Partially Linear Models

In this paper, we introduce a family of robust estimates for the parametric and nonparametric components under a generalized partially linear model, where the data are modeled by $y_{i}|({\bf x}_{i},t_{i})\sim F(\cdot,\mu _{i})$ with $\mu _{i}=H(\eta (t_{i})+{\bf x}_{i}^{{\rm T}}\beta)$, for some known distribution function F and link function H. It is shown that the estimates of . are root-n consistent and asymptotically normal. Through a Monte Carlo study, the performance of these estimators is compared with that of the classical ones.