Modeling large scale species abundance with latent spatial processes

DOI10.1214/10-AOAS335zbMath1202.62168arXiv1011.3327WikidataQ56937536 ScholiaQ56937536MaRDI QIDQ614156

Adam M. Wilson, John A. jun. Silander, Avishek Chakraborty, Andrew M. Latimer, Alan E. Gelfand

Publication date: 27 December 2010

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

Full work available at URL: https://arxiv.org/abs/1011.3327

zbMATH Keywords

parallel computinglatent variablesmisalignmentconditional autoregressive priorordinal categorical data

Mathematics Subject Classification ID

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

Related Items

Spatial Misalignment Models for Small Area Estimation: A Simulation Study, Bayesian modeling of discrete-time point-referenced spatio-temporal data, Spatial regression modeling for compositional data with many zeros, Multilevel latent Gaussian process model for mixed discrete and continuous multivariate response data, An adaptive spatial model for precipitation data from multiple satellites over large regions

Uses Software

• spBayes

Cites Work

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