Additive isotone regression
Statistics Theory
2007-09-12 v1 Statistics Theory
Abstract
This paper is about optimal estimation of the additive components of a nonparametric, additive isotone regression model. It is shown that asymptotically up to first order, each additive component can be estimated as well as it could be by a least squares estimator if the other components were known. The algorithm for the calculation of the estimator uses backfitting. Convergence of the algorithm is shown. Finite sample properties are also compared through simulation experiments.
Cite
@article{arxiv.0709.0888,
title = {Additive isotone regression},
author = {Enno Mammen and Kyusang Yu},
journal= {arXiv preprint arXiv:0709.0888},
year = {2007}
}
Comments
Published at http://dx.doi.org/10.1214/074921707000000355 in the IMS Lecture Notes Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org)