The paper shows that certain types of estimators used in regression models are consistent and asymptotically normal. These estimators can accurately estimate various aspects of the regression function, even when the errors in the model vary in their characteristics. The results apply to different types of regression models, including nonparametric, semiparametric, and additive interactive regression models. The findings are based on a general set of results for series estimators.