On Subset-Selection in Nonparametric Stochastic Regration

Yao, Q.W and Tong, H.W (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 available from this repository)

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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: Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: P. Ogbuji
Date Deposited: 09 Jun 2009 11:22
Last Modified: 09 Jun 2009 11:22
Resource URI: http://kar.kent.ac.uk/id/eprint/20120 (The current URI for this page, for reference purposes)
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