Careful prior specification avoids incautious inference for log-Gaussian Cox point processes

Sorbye Sigrunn H.; Illian Janine B.; Simpson Daniel P.; Burslem David; Rue Havard

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Careful prior specification avoids incautious inference for log-Gaussian Cox point processes

机译：谨慎的先验规格避免了对数高斯Cox点过程的错误推断

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Hyperprior specifications for random fields in spatial point process modelling can have a major influence on the results. In fitting log-Gaussian Cox processes to rainforest tree species, we consider a reparameterized model combining a spatially structured and an unstructured random field into a single component. This component has one hyperparameter accounting for marginal variance, whereas an additional hyperparameter governs the fraction of the variance that is explained by the spatially structured effect. This facilitates interpretation of the hyperparameters, and significance of covariates is studied for a range of hyperprior specifications. Appropriate scaling makes the analysis invariant to grid resolution.

机译：空间点过程建模中对随机字段的超先验规范可能会对结果产生重大影响。在将log-Gaussian Cox过程拟合到热带雨林树种时，我们考虑将空间结构化和非结构化随机场组合为单个分量的重新参数化模型。该组件具有一个用于解释边际方差的超参数，而另一个超参数则控制由空间结构效应解释的方差的一部分。这有助于解释超参数，并且针对一系列超先验规范研究了协变量的意义。适当的缩放比例使分析不变于网格分辨率。

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《Journal of the royal statistical society》 |2019年第3期|543-564|共22页
作者
Sorbye Sigrunn H.; Illian Janine B.; Simpson Daniel P.; Burslem David; Rue Havard;
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作者单位

UiT Arctic Univ Norway, Tromso, Norway;

Univ St Andrews, St Andrews, Fife, Scotland;

Univ Toronto, Toronto, ON, Canada;

Univ Aberdeen, Aberdeen, Scotland;

King Abdullah Univ Sci & Technol, Thuwal, Saudi Arabia;

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正文语种 eng
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关键词
Bayesian analysis; Penalized complexity prior; R-INLA; Spatial modelling; Spatial point process;

机译：贝叶斯分析;归纳复杂度优先;R-INLA;空间建模;空间点过程;

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专利

1. Going off grid: computationally efficient inference for log-Gaussian Cox processes [J] . Simpson D., Illian J. B., Lindgren F., Biometrika . 2016,第1期

机译：脱离网格：对数高斯Cox流程的计算有效推断
2. Bayesian Inference and Data Augmentation Schemes for Spatial, Spatiotemporal and Multivariate Log-Gaussian Cox Processes in R [J] . Benjamin M. Taylor, Tilman M. Davies, Barry S. Rowlingson, Journal of Statistical Software . 2015,第1期

机译：R中空间，时空和多元对数高斯Cox过程的贝叶斯推断和数据增强方案
3. lgcp: An R Package for Inference with Spatial and Spatio-Temporal Log-Gaussian Cox Processes [J] . Benjamin M. Taylor, Tilman M. Davies, Barry S. Rowlingson, Journal of Statistical Software . 2013,第1期

机译：lgcp：用于时空对数对数高斯Cox流程推理的R包
4. Modeling Tweet Arrival Times using Log-Gaussian Cox Processes [C] . Michal Lukasik, P.K. Srijith, Trevor Cohn, Conference on empirical methods in natural language processing . 2015

机译：使用对数高斯Cox流程建模推文到达时间
5. Optimal predictive inference in log-Gaussian random fields. [D] . Rui, Changxiang. 2009

机译：对数高斯随机字段中的最优预测推断。
6. Bayesian Computation for Log-Gaussian Cox Processes: A Comparative Analysis of Methods [O] . Ming Teng, Farouk S. Nathoo, Timothy D. Johnson -1

机译：对数-高斯Cox过程的贝叶斯计算：方法的比较分析
7. Bayesian inference and data augmentation schemes for spatial, spatiotemporal and multivariate Log-Gaussian Cox processes in R [O] . Taylor Benjamin, Davies Tilman, Rowlingson Barry, 2015

机译：R中空间，时空和多元Log-Gaussian Cox过程的贝叶斯推断和数据扩充方案
8. Inference for Thinned Point Processes, with Application to Cox Processes. [R] . Karr, A. F., Krishnaiah, P. R. 1985

机译：对应用于Cox过程的细化点过程的推断。

Careful prior specification avoids incautious inference for log-Gaussian Cox point processes

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