Modern statistics for spatial point processes

被引：188

作者：

Moller, Jesper ^{[1
]}

Waagepetersen, Rasmus P. ^{[1
]}

机构：

[1] Aalborg Univ, Dept Math Sci, Aalborg, Denmark

来源：

SCANDINAVIAN JOURNAL OF STATISTICS | 2007年 / 34卷 / 04期

关键词：

Bayesian inference; conditional intensity; Cox process; Gibbs point process; Markov chain Monte Carlo; maximum likelihood; perfect simulation; Poisson process; residuals; simulation free estimation; summary statistics;

D O I：

10.1111/j.1467-9469.2007.00569.x

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 summarize and discuss the current state of spatial point process theory and directions for future research, making an analogy with generalized linear models and random effect models, and illustrating the theory with various examples of applications. In particular, we consider Poisson, Gibbs and Cox process models, diagnostic tools and model checking, Markov chain Monte Carlo algorithms, computational methods for likelihood-based inference, and quick non-likelihood approaches to inference.

引用

页码：643 / 684

页数：42

共 136 条

[1]

[Anonymous], 2003, Statistical Inference and Simulation for Spatial Point Processes

[2] Residual analysis for spatial point processes [J].