A Bias Correction for Cross‐validation Bandwidth Selection when a Kernel Estimate is Based on Dependent Data

Martin Skold

Journal of Time Series Analysis, 2001, vol. 22, issue 4, 493-503

Abstract: Least‐squares cross‐validation (LSCV) bandwidth selection for kernel density estimation has been shown to underestimate the optimal bandwidth if data are positively correlated. We calculate the asymptotic bias for the LSCV criterion under a continuous‐time model and apply it as a correction term to discrete‐time data that can be modeled as a smooth continuous‐time process sampled at a high rate.

Date: 2001
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/1467-9892.00237

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jtsera:v:22:y:2001:i:4:p:493-503

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0143-9782

Access Statistics for this article

Journal of Time Series Analysis is currently edited by M.B. Priestley

More articles in Journal of Time Series Analysis from Wiley Blackwell
Bibliographic data for series maintained by Wiley Content Delivery ().