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    Spatial kernel regression estimation: weak consistency.

    Access Status
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    Authors
    Lu, Zudi
    Chen, X.
    Date
    2004
    Type
    Journal Article
    
    Metadata
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    Citation
    Lu, Zudi and Chen, Xing. 2004. Spatial kernel regression estimation: weak consistency. Statistics and Probability Letters. 68 (2): pp. 125-136.
    Source Title
    Statistics and Probability Letters
    DOI
    10.1016/j.spl.2003.08.014
    ISSN
    01677152
    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/505573/description#description. Copyright © 2004 Elsevier B.V. All rights reserved

    URI
    http://hdl.handle.net/20.500.11937/13839
    Collection
    • Curtin Research Publications
    Abstract

    In this paper, we introduce a kernel method to estimate a spatial conditional regression under mixing spatial processes. Some preliminary statistical properties including weak consistency and convergence rates are investigated. The sufficient conditions on mixing coefficients and the bandwidth are established to ensure distribution-free weak consistency, which requires no assumption on the regressor and allows the mixing coefficients decreasing to zero slowly. However, to achieve an optimal convergence rate, some requirements on the regressor and the decreasing rate of mixing coefficients tending to zero are needed.

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