Hierarchical spatial models for predicting tree species assemblages across large domains

DOI10.1214/09-AOAS250zbMath1196.62121arXiv0910.1490OpenAlexW3101573030WikidataQ33547402 ScholiaQ33547402MaRDI QIDQ985023

Sudipto Banerjee, Ronald E. McRoberts, Andrew O. Finley

Publication date: 20 July 2010

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0910.1490

zbMATH Keywords

Markov chain Monte Carlologistic regressionBayesian inferencespatial predictive processspatially-varying coefficientsspecies assemblages

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of statistics to environmental and related topics (62P12) Bayesian inference (62F15) Numerical analysis or methods applied to Markov chains (65C40)

Related Items

Spatial data compression via adaptive dispersion clustering, Assessing fit in Bayesian models for spatial processes, Approximate Bayesian inference for large spatial datasets using predictive process models, Modeling complex spatial dependencies: low-rank spatially varying cross-covariances with application to soil nutrient data, Hierarchical spatial models for predicting tree species assemblages across large domains

Uses Software

• gss
• SemiPar
• BayesDA
• FRK
• clusfind

Cites Work

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