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Direct Estimation of Low Dimensional Components in Additive Models

Fan, Jianqing ; Härdle, Wolfgang ; Mammen, Enno

In: The annals of statistics, 26 (1998), Nr. 3. pp. 943-971. ISSN 0090-5364

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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.

Item Type: Article
Journal or Publication Title: The annals of statistics
Volume: 26
Number: 3
Publisher: IMS Business Office
Place of Publication: Hayward, Calif.
Date Deposited: 07 Jun 2016 08:05
Date: 1998
ISSN: 0090-5364
Page Range: pp. 943-971
Faculties / Institutes: The Faculty of Mathematics and Computer Science > Department of Applied Mathematics
Subjects: 310 General statistics
510 Mathematics
Schriftenreihe ID: Beiträge zur Statistik > Beiträge
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