Asymptotically efficient estimators for nonparametric heteroscedastic regression models
نویسنده
چکیده
This paper concerns the estimation of a function at a point in nonparametric heteroscedastic regression models with Gaussian noise or noise having unknown distribution. In those cases an asymptotically efficient kernel estimator is constructed for the minimax absolute error risk.
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