Rate-optimal estimation for a general class of nonparametric regression models with unknown link functions

dc.creatorHorowitz, Joel L.
dc.creatorMammen, Enno
dc.date2008-03-20
dc.date.accessioned2026-07-07T12:17:47Z
dc.date.available2026-07-07T12:17:47Z
dc.descriptionThis paper discusses a nonparametric regression model that naturally generalizes neural network models. The model is based on a finite number of one-dimensional transformations and can be estimated with a one-dimensional rate of convergence. The model contains the generalized additive model with unknown link function as a special case. For this case, it is shown that the additive components and link function can be estimated with the optimal rate by a smoothing spline that is the solution of a penalized least squares criterion.
dc.descriptionPublished in at http://dx.doi.org/10.1214/009053607000000415 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0803.2999
dc.identifierhttp://arxiv.org/abs/0803.2999
dc.identifierAnnals of Statistics 2007, Vol. 35, No. 6, 2589-2619
dc.identifierdoi:10.1214/009053607000000415
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/212193
dc.subjectStatistics Theory
dc.subject62G08 (Primary) 62G20 (Secondary)
dc.titleRate-optimal estimation for a general class of nonparametric regression models with unknown link functions
dc.typetext

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