Translator Disclaimer
June 1998 Direct estimation of low-dimensional components in additive models
Jianqing Fan, Wolfgang Härdle, Enno Mammen
Ann. Statist. 26(3): 943-971 (June 1998). DOI: 10.1214/aos/1024691083

Abstract

Additive regression models have turned out to be a useful statistical tool in analyses of high-dimensional data sets. Recently, an estimator of additive components has been introduced by Linton and Nielsen which is based on marginal integration. The explicit definition of this estimator makes possible a fast computation and allows an asymptotic distribution theory. In this paper an asymptotic treatment of this estimate is offered for several models. A modification of this procedure is introduced. We consider weighted marginal integration for local linear fits and we show that this estimate has the following advantages.

(i) With an appropriate choice of the weight function, the additive components can be efficiently estimated: An additive component can be estimated with the same asymptotic bias and variance as if the other components were known.

(ii) Application of local linear fits reduces the design related bias.

Citation

Download Citation

Jianqing Fan. Wolfgang Härdle. Enno Mammen. "Direct estimation of low-dimensional components in additive models." Ann. Statist. 26 (3) 943 - 971, June 1998. https://doi.org/10.1214/aos/1024691083

Information

Published: June 1998
First available in Project Euclid: 21 June 2002

zbMATH: 1073.62527
MathSciNet: MR1635422
Digital Object Identifier: 10.1214/aos/1024691083

Rights: Copyright © 1998 Institute of Mathematical Statistics

JOURNAL ARTICLE
29 PAGES


SHARE
Vol.26 • No. 3 • June 1998
Back to Top