Estimation of the Asymptotic Variance of Kernel Density Estimators for Continuous Time Processes

Armelle Guillou and Florence Merlevède

Journal of Multivariate Analysis, 2001, vol. 79, issue 1, 114-137

Abstract: In order to construct confidence sets for a marginal density f of a strictly stationary continuous time process observed over the time interval [0, T], it is necessary to have at one's disposal a Central Limit Theorem for the kernel density estimator fT. In this paper we address the question of nonparametric estimation of the asymptotic variance of  fT, an unknown quantity dependent on f. We construct two estimators and study their asymptotic properties.

Keywords: kernel estimator; continuous processes strong mixing sequences confidence sets (search for similar items in EconPapers)
Date: 2001
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0047-259X(00)91958-4
Full text for ScienceDirect subscribers only

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:eee:jmvana:v:79:y:2001:i:1:p:114-137

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Journal of Multivariate Analysis is currently edited by de Leeuw, J.

More articles in Journal of Multivariate Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().