Objective Bayesian analysis of spatial data with uncertain nugget and range parameters

DOI10.1002/cjs.11132zbMath1348.62082OpenAlexW2036667289MaRDI QIDQ2856543

Hannes Kazianka, Jürgen Pilz

Publication date: 29 October 2013

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/cjs.11132

zbMATH Keywords

Gaussian processposterior proprietyreference priorJeffreys priorfrequentist propertiesnugget effect

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Random fields; image analysis (62M40) Applications of statistics to environmental and related topics (62P12) Bayesian inference (62F15)

Related Items (7)

Objective Bayesian analysis of geometrically anisotropic spatial data ⋮ Robust Gaussian stochastic process emulation ⋮ Optimal compromise between incompatible conditional probability distributions, with application to Objective Bayesian Kriging ⋮ Propriety of the reference posterior distribution in Gaussian process modeling ⋮ Robust additive Gaussian process models using reference priors and cut-off-designs ⋮ Constructing Priors that Penalize the Complexity of Gaussian Random Fields ⋮ Jointly robust prior for Gaussian stochastic process in emulation, calibration and variable selection

Uses Software

• R-Geo
• spBayes
• BayesDA
• EnviroStat

Cites Work

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• The Selection of Prior Distributions by Formal Rules
• Bayesian Prediction of Transformed Gaussian Random Fields
• Objective Bayesian Analysis of Spatially Correlated Data
• Directional Rates of Change Under Spatial Process Models
• A note on the correlation structure of transformed Gaussian random fields
• Objective Bayesian analysis of spatial data with measurement error
• Mathematical analysis II. Transl. from the 4th Russian edition by Roger Cooke

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