Non- and semi-parametric estimation of interaction in inhomogeneous point patterns

被引:402
作者
Baddeley, AJ
Moller, J
Waagepetersen, R
机构
[1] Univ Western Australia, Dept Math & Stat, Nedlands, WA 6907, Australia
[2] Univ Aalborg, Dept Math, DK-9220 Aalborg, Denmark
关键词
kernel estimation; Markov chain Monte Carlo; Markov point process; second-order methods; semi-parametric inference; spatial statistics; summary statistics; thinning;
D O I
10.1111/1467-9574.00144
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
We develop methods for analysing the 'interaction' or dependence between points in a spatial point pattern, when the pattern is spatially inhomogeneous. Completely non-parametric study of interactions is possible using an analogue of the K-function. Alternatively one may assume a semi-parametric model in which a (parametrically specified) homogeneous Markov point process is subjected to (non-parametric) inhomogeneous independent thinning. The effectiveness of these approaches is tested on datasets representing the positions of trees in forests.
引用
收藏
页码:329 / 350
页数:22
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