Hierarchical Models for Spatial Data with Errors that are Correlated with the Latent Process

DOI10.5705/ss.202016.0230zbMath1444.62066OpenAlexW2811174083MaRDI QIDQ5220360

Jonathan R. Bradley, Christopher K. Wikle, Scott H. Holan

Publication date: 16 March 2020

Published in: Statistica Sinica (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/8ada86f4adc007ffedc53029dc297677feefec0e

zbMATH Keywords

parsimonyBayesian inferencemachine learningnonresponselow rankmixed effects model

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Applications of statistics to economics (62P20) Inference from stochastic processes and prediction (62M20) Bayesian inference (62F15)

Related Items (5)

A Fused Gaussian Process Model for Very Large Spatial Data ⋮ An Approach to Incorporate Subsampling Into a Generic Bayesian Hierarchical Model ⋮ Latent multivariate log-gamma models for high-dimensional multitype responses with application to daily fine particulate matter and mortality counts ⋮ Joint Bayesian analysis of multiple response-types using the hierarchical generalized transformation model ⋮ Modeling Dependence in Spatio-Temporal Econometrics

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