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On Subset-Selection in Nonparametric Stochastic Regration

Yao, Qiwei, Tong, Howell (1994) On Subset-Selection in Nonparametric Stochastic Regration. Statistica Sinica, 4 (1). pp. 51-70. ISSN 1017-0405. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:20120)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.

Abstract

This paper is concerned with the use of a cross-validation method based on the kernel estimate of the conditional mean for the subset selection of stochastic regressors within the framework of non-linear stochastic regression. Under the assumption that the observations are strictly stationary and absolutely regular, we show that the cross-validatory selection is consistent. Furthermore, two kinds of asymptotic efficiency of the selected model are proved. Both simulated and real data are used as illustrations.

Item Type: Article
Uncontrolled keywords: ABSOLUTELY REGULAR; CROSS-VALIDATION; EFFICIENCY; KERNEL ESTIMATION; HETEROSCEDASTICITY; NONLINEAR STOCHASTIC REGRESSION; SUBSET SELECTION
Subjects: H Social Sciences > HA Statistics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: P. Ogbuji
Date Deposited: 09 Jun 2009 11:22 UTC
Last Modified: 16 Nov 2021 09:58 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/20120 (The current URI for this page, for reference purposes)

University of Kent Author Information

Yao, Qiwei.

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Tong, Howell.

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