Nonparametric Estimation of an Additive Model With a Link Function

dc.creatorHorowitz, Joel L.
dc.creatorMammen, Enno
dc.date2005-08-30
dc.date.accessioned2026-07-07T08:07:14Z
dc.date.available2026-07-07T08:07:14Z
dc.descriptionThis paper describes an estimator of the additive components of a nonparametric additive model with a known link function. When the additive components are twice continuously differentiable, the estimator is asymptotically normally distributed with a rate of convergence in probability of n^{-2/5}. This is true regardless of the (finite) dimension of the explanatory variable. Thus, in contrast to the existing asymptotically normal estimator, the new estimator has no curse of dimensionality. Moreover, the estimator has an oracle property. The asymptotic distribution of each additive component is the same as it would be if the other components were known with certainty.
dc.descriptionPublished at http://dx.doi.org/10.1214/009053604000000814 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/math/0508595
dc.identifierhttp://arxiv.org/abs/math/0508595
dc.identifierAnnals of Statistics 2004, Vol. 32, No. 6, 2412-2443
dc.identifierdoi:10.1214/009053604000000814
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/130876
dc.subjectStatistics Theory
dc.subject62G08 (Primary) 62G20 (Secondary)
dc.titleNonparametric Estimation of an Additive Model With a Link Function
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