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. | |
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 |
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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: | 16 Nov 2021 09:55 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/17289 (The current URI for this page, for reference purposes) |
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