Nonparametric estimation of the dependence of a spatial point process on spatial covariates

被引：2
|
作者：
Baddeley, Adrian [1 ,2 ]
Chang, Ya-Mei [3 ]
Song, Yong [4 ]
Turner, Rolf [5 ]
机构：
[1] CSIRO Math Informat & Stat, Perth, WA, Australia
[2] Univ Western Australia, Sch Math & Stat, Nedlands, WA 6009, Australia
[3] Tamkang Univ, Dept Stat, Taipei, Taiwan
[4] CSIRO Land & Water Highett, Melbourne, Vic, Australia
[5] Univ Auckland, Dept Stat, Auckland 1, New Zealand
来源：
STATISTICS AND ITS INTERFACE | 2012年 / 5卷 / 02期
关键词：
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; DENSITY-ESTIMATION; CONFIDENCE-INTERVALS; REGRESSION; DISEASE; RISK; PROBABILITY; BOOTSTRAP;
D O I：
暂无
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
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 non-parametric (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.
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页码：221 / 236
页数：16
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