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dc.contributor.authorHallin, M.
dc.contributor.authorLu, Zudi
dc.contributor.authorTran, L.
dc.date.accessioned2017-01-30T11:17:31Z
dc.date.available2017-01-30T11:17:31Z
dc.date.created2010-03-29T20:04:53Z
dc.date.issued2004
dc.identifier.citationHallin, Marc and Lu, Zudi and Tran, Lanh T. 2004. Kernel density estimation for spatial processes: the L_1 theory. Journal of Multivariate Analysis. 88 (1): pp. 61-75.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/10233
dc.identifier.doi10.1016/S0047-259X(03)00060-5
dc.description.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.

dc.publisherElsevier
dc.subjectSpatial linear or nonlinear processes
dc.subjectL1 theory
dc.subjectKernel density estimator
dc.subjectBandwidth
dc.titleKernel density estimation for spatial processes: the L_1 theory
dc.typeJournal Article
dcterms.source.volume88
dcterms.source.startPage61
dcterms.source.endPage75
dcterms.source.issn0047-259X
dcterms.source.titleJournal of Multivariate Analysis
curtin.note

The link to the journal’s home page is: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description. Copyright © 2004 Elsevier B.V. All rights reserved

curtin.accessStatusOpen access via publisher
curtin.facultySchool of Science and Computing
curtin.facultyDepartment of Mathematics and Statistics
curtin.facultyFaculty of Science and Engineering


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