Kernel density estimation for spatial processes: the L_1 theory
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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.
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
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