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

Authors:
Man-Suk Oh;Dong Wan Shin;Han Joon Kim
Affiliations:
Department of Statistics, Ewha Womans University, So-Dae-Moon Gu, Seoul, South Korea;Department of Statistics, Ewha Womans University, So-Dae-Moon Gu, Seoul, South Korea;Korea Ocean Research & Development Institute, Ansan Kyung-Gi-Do, South Korea
Venue:
Computational Statistics & Data Analysis
Year:
2002

Citing 1
Cited 1

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An O(N) parallel method of computing the Log-Jacobian of the variable transformation for models with spatial interaction on a lattice

Computational Statistics & Data Analysis

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Abstract

A Bayesian approach is proposed for estimating regression models on rectangular grids in which errors are spatially correlated and missing observations are present in the response variable. An easy and efficient Markov chain Monte Carlo algorithm is fully described for posterior inference on parameters and prediction of missing observations. Analysis of a real marine remote-sensing data set is presented to illustrate the method.