Rate-optimal estimation for a general class of nonparametric regression models with unknown link functions
Statistics Theory
2008-12-18 v1 Statistics Theory
Abstract
This 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.
Cite
@article{arxiv.0803.2999,
title = {Rate-optimal estimation for a general class of nonparametric regression models with unknown link functions},
author = {Joel L. Horowitz and Enno Mammen},
journal= {arXiv preprint arXiv:0803.2999},
year = {2008}
}
Comments
Published 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)