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

Cited 7 time in WEB OF SCIENCE Cited 8 time in Scopus
Title
Bayesian analysis of regression models with spatially correlated errors and missing observations
Author(s)
Oh, MS; Shin, DW; Kim, HJ
Publication Year
2002-06-28
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. (C) 2002 Elsevier Science B.V. All rights reserved.
ISSN
0167-9473
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/5691
DOI
10.1016/S0167-9473(01)00084-6
Bibliographic Citation
COMPUTATIONAL STATISTICS & DATA ANALYSIS, v.39, no.4, pp.387 - 400, 2002
Publisher
ELSEVIER SCIENCE BV
Subject
TIME-SERIES MODELS; FIELD EXPERIMENTS; TRIALS
Keywords
Gibbs sampling algorithm; Markov chain Monte Carlo; missing value; posterior inference; spatial data
Type
Article
Language
English
Document Type
Article
Publisher
ELSEVIER SCIENCE BV
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