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    Kernel density estimation for spatial processes: the L_1 theory

    Access Status
    Open access via publisher
    Authors
    Hallin, M.
    Lu, Zudi
    Tran, L.
    Date
    2004
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Hallin, 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.
    Source Title
    Journal of Multivariate Analysis
    DOI
    10.1016/S0047-259X(03)00060-5
    ISSN
    0047-259X
    Faculty
    School of Science and Computing
    Department of Mathematics and Statistics
    Faculty of Science and Engineering
    Remarks

    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

    URI
    http://hdl.handle.net/20.500.11937/10233
    Collection
    • Curtin Research Publications
    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.

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