Estimation in additive models with highly or nonhighly correlated covariates
Abstract
Motivated by normalizing DNA microarray data and by predicting the interest rates, we explore nonparametric estimation of additive models with highly correlated covariates. We introduce two novel approaches for estimating the additive components, integration estimation and pooled backfitting estimation. The former is designed for highly correlated covariates, and the latter is useful for nonhighly correlated covariates. Asymptotic normalities of the proposed estimators are established. Simulations are conducted to demonstrate finite sample behaviors of the proposed estimators, and real data examples are given to illustrate the value of the methodology.
Cite
@article{arxiv.1010.0320,
title = {Estimation in additive models with highly or nonhighly correlated covariates},
author = {Jiancheng Jiang and Yingying Fan and Jianqing Fan},
journal= {arXiv preprint arXiv:1010.0320},
year = {2010}
}
Comments
Published in at http://dx.doi.org/10.1214/09-AOS753 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)