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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/78142

    Title: Nonparametric estimation of the dependence of a spatial point process on spatial covariates
    Authors: Baddeley, Adrian;Chang, Ya-Mei;Song, Yong;Turner, Rolf
    Contributors: 淡江大學統計學系
    Keywords: confidence intervals;density estimation;kernel smoothing;local likelihood;logistic regression;point process intensity;poisson point process;[geological] prospectivity mapping;spatial covariates;relative distributions;resource selection function;weighted distribution
    Date: 2012-06
    Issue Date: 2012-09-05 16:54:12 (UTC+8)
    Publisher: Somerville: International Press
    Abstract: In the statistical analysis of spatial point patterns, it is often important to investigate whether the point pattern depends on spatial covariates. This paper describes nonparametric (kernel and local likelihood) methods for estimating the effect of spatial covariates on the point process intensity. Variance estimates and confidence intervals are provided in the case of a Poisson point process. Techniques are demonstrated with simulated examples and with applications to exploration geology and forest ecology.
    Relation: Statistics and Its Interface 5(2), pp.221-236
    DOI: 10.4310/SII.2012.v5.n2.a7
    Appears in Collections:[Graduate Institute & Department of Statistics] Journal Article

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