Kernel density estimation for spatial processes: the L1 theory
Marc Hallin,
Zudi Lu and
Lanh T. Tran
Journal of Multivariate Analysis, 2004, vol. 88, issue 1, 61-75
Abstract:
The purpose of this paper is to investigate kernel density estimators for spatial processes with linear or nonlinear structures. Sufficient conditions for such estimators to converge in L1 are obtained under extremely general, verifiable conditions. The results hold for mixing as well as for nonmixing processes. Potential applications include testing for spatial interaction, the spatial analysis of causality structures, the definition of leading/lagging sites, the construction of clusters of comoving sites, etc.
Keywords: Bandwidth; Kernel; density; estimator; L1; theory; Spatial; linear; or; nonlinear; processes (search for similar items in EconPapers)
Date: 2004
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (25)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0047-259X(03)00060-5
Full text for ScienceDirect subscribers only
Related works:
Working Paper: Kernel density estimation for spatial processes: the L1 theory (2004)
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:88:y:2004:i:1:p:61-75
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 ().