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

Man Suk Oh; Dong Wan Shin; Han Joon Kim

doi:10.1016/S0167-9473(01)00084-6

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

Man Suk Oh, Dong Wan Shin, Han Joon Kim

Department of Statistics

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

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
Pages (from-to)	387-400
Number of pages	14
Journal	Computational Statistics and Data Analysis
Volume	39
Issue number	4
DOIs	https://doi.org/10.1016/S0167-9473(01)00084-6
State	Published - 28 Jun 2002

Bibliographical note

Funding Information:
The authors are very grateful for the valuable comments of the three referees and the previous Editor, Dr. Naeve. Also, we are indebted to Professor Paul Green for the careful proofreading of this paper. The first author was supported by 1997 Ewha Womans University Research Fund.

Keywords

Gibbs sampling algorithm
Markov chain Monte Carlo
Missing value
Posterior inference
Spatial data

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/S0167-9473(01)00084-6

Cite this

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AU - Shin, Dong Wan

AU - Kim, Han Joon

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AB - 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.

KW - Gibbs sampling algorithm

KW - Markov chain Monte Carlo

KW - Missing value

KW - Posterior inference

KW - Spatial data

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JO - Computational Statistics and Data Analysis

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ER -

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

Abstract

Bibliographical note

Keywords

UN SDGs

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