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Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

Inference on the primary parameter of interest with the aid of dimension reduction estimation

Language

English

Abstract

As high dimensional data become routinely available in applied sciences, sufficient dimension reduction has been widely employed and its research has received considerable attention. However, with the majority of sufficient dimension reduction methodology focusing on the dimension reduction step, complete analysis and inference after dimension reduction have yet to receive much attention. We couple the strategy of sufficient dimension reduction with a flexible semiparametric model. We concentrate on inference with respect to the primary variables of interest, and we employ sufficient dimension reduction to bring down the dimension of the regression effectively. Extensive simulations demonstrate the efficacy of the method proposed, and a real data analysis is presented for illustration. © 2010 Royal Statistical Society.

Keywords

Central partial mean subspace, Dimension reduction, Partial ordinary least squares, Partially linear single-index model

Publication Date

2011

Source Publication Title

Journal of the Royal Statistical Society: Series B

Volume

73

Issue

1

Start Page

59

End Page

80

Publisher

Royal Statistical Society

ISSN (print)

13697421

ISSN (electronic)

14679868

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