Asymptotically efficient estimators for nonparametric heteroscedastic regression models

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dc.creatorBrua, Jean-Yves
dc.date2007-11-29
dc.date.accessioned2026-07-07T08:46:02Z
dc.date.available2026-07-07T08:46:02Z
dc.descriptionThis paper concerns the estimation of the regression function at a given point in nonparametric heteroscedastic models with Gaussian noise or with noise having unknown distribution. In the two cases an asymptotically efficient kernel estimator is constructed for the minimax absolute error risk.
dc.identifierhttps://arxiv.org/abs/0711.4725
dc.identifierhttp://arxiv.org/abs/0711.4725
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/143152
dc.subjectStatistics Theory
dc.subject62G08; 62G20
dc.titleAsymptotically efficient estimators for nonparametric heteroscedastic regression models
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