Bayesian Variable Selection Methods for Log-Gaussian Cox Processes

DOI10.1007/978-3-319-91143-4_10zbMath1397.62091OpenAlexW2839208562MaRDI QIDQ4689192

Patrícia Viana da Silva, Jony Arrais Pinto Junior

Publication date: 15 October 2018

Published in: Springer Proceedings in Mathematics & Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-91143-4_10

zbMATH Keywords

point patternBayesian variable selectionlog-Gaussian Cox process

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Bayesian inference (62F15) Statistical ranking and selection procedures (62F07)

Uses Software

• WinBUGS

Cites Work

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• On Bayesian model and variable selection using MCMC
• Analysis of Minnesota colon and rectum cancer point patterns with spatial and nonspatial covariate information
• Estimating the dimension of a model
• Convergence of posteriors for discretized log Gaussian Cox processes.
• Space-time Multi Type Log Gaussian Cox Processes with a View to Modelling Weeds
• A CASE STUDY ON POINT PROCESS MODELLING IN DISEASE MAPPING
• A dynamic approach to the statistical analysis of point processes
• Log Gaussian Cox Processes
• A review of Bayesian variable selection methods: what, how and which
• A new look at the statistical model identification

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