Bayesian analysis of regression models with spatially correlated errors and missing observations

DOI10.1016/S0167-9473(01)00084-6zbMath0993.62025OpenAlexW2061007428MaRDI QIDQ1603673

Man-Suk Oh, Dong Wan Shin, Han Joon Kim

Publication date: 15 July 2002

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-9473(01)00084-6

zbMATH Keywords

Markov chain Monte Carlospatial dataGibbs sampling algorithmmissing valuesposterior inference

Mathematics Subject Classification ID

Related Items (4)

Bayesian spatial regression models with closed skew normal correlated errors and missing observations ⋮ An \(O(N)\) parallel method of computing the log-Jacobian of the variable transformation for models with spatial interaction on a lattice ⋮ ML estimation and an efficiency study for mean estimators in spatially correlated repeated arrays ⋮ Performance of extrapolation based on Pitman's measure of closeness in spatial regression models with extended skew t innovations

Uses Software

• IMSL Numerical Libraries

Cites Work

• Sampling-Based Approaches to Calculating Marginal Densities
• Estimation and Identification of Space-Time ARMAX Models in the Presence of Missing Data
• Statistical Analysis of Field Experiments Using Neighbouring Plots
• Inference and missing data
• A subclass of lattice processes applied to a problem in planar sampling
• Regression Models with Spatially Correlated Errors
• BAYESIAN ANALYSIS OF AUTOREGRESSIVE TIME SERIES VIA THE GIBBS SAMPLER
• Application of em-type algorithms to spatial data
• Parameter estimation in regression models with autocorrelated errors using irregular data
• Monte Carlo sampling methods using Markov chains and their applications
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