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

Yao, Qiwei and 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. (The full text of this publication is not available from this repository)

The full text of this publication is not available from this repository. (Contact us about this Publication)

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
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
Last Modified: 25 Jun 2014 09:33
Resource URI: http://kar.kent.ac.uk/id/eprint/17289 (The current URI for this page, for reference purposes)
  • Depositors only (login required):