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Cross-validatory bandwidth selections for regression estimation based on dependent data

Yao, Qiwei, Tong, Howell (1998) Cross-validatory bandwidth selections for regression estimation based on dependent data. Journal of Statistical Planning and Inference, 68 (2). pp. 387-415. ISSN 0378-3758. (doi:10.1016/S0378-3758(97)00151-1) (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:17289)

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. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1016/S0378-3758(97)00151-1

Abstract

We suggest a simple and fast method to determine the bandwidth in kernel regression. The method can be viewed as a generalized cross-validation. We have proved asymptotic optimality of the proposed bandwidth selector under the assumption that the observations are strictly stationary and rho-mixing. Simulation has been conducted to compare the performance of various cross-validation bandwidth selectors applied to dependent data, which shows that the ordinary cross-validation method is quite stable in regression estimation with random design even when the data are highly correlated.

Item Type: Article
DOI/Identification number: 10.1016/S0378-3758(97)00151-1
Uncontrolled keywords: bandwidth; cross-validation; kernel estimation; locally linear regression; rho-mixing
Subjects: Q Science > QA Mathematics (inc Computing science) > QA273 Probabilities
Depositing User: Tara Puri
Date Deposited: 24 Mar 2009 11:12 UTC
Last Modified: 14 Jun 2019 13:45 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/17289 (The current URI for this page, for reference purposes)
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