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.
Original language | English |
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Pages (from-to) | 387-400 |
Number of pages | 14 |
Journal | Computational Statistics and Data Analysis |
Volume | 39 |
Issue number | 4 |
DOIs | |
State | Published - 28 Jun 2002 |
Keywords
- Gibbs sampling algorithm
- Markov chain Monte Carlo
- Missing value
- Posterior inference
- Spatial data