Browse > Article
http://dx.doi.org/10.5322/JES.2011.20.12.1541

Uncertainty Analysis of Parameters of Spatial Statistical Model Using Bayesian Method for Estimating Spatial Distribution of Probability Rainfall  

Seo, Young-Min (Department of Civil Engineering, Yeungnam University)
Park, Ki-Bum (Department of Construction Information Andong Science College)
Kim, Sung-Won (Department of Railroad Civil Engineering, Dongyang University)
Publication Information
Journal of Environmental Science International / v.20, no.12, 2011 , pp. 1541-1551 More about this Journal
Abstract
This study applied the Bayesian method for the quantification of the parameter uncertainty of spatial linear mixed model in the estimation of the spatial distribution of probability rainfall. In the application of Bayesian method, the prior sensitivity analysis was implemented by using the priors normally selected in the existing studies which applied the Bayesian method for the puppose of assessing the influence which the selection of the priors of model parameters had on posteriors. As a result, the posteriors of parameters were differently estimated which priors were selected, and then in the case of the prior combination, F-S-E, the sizes of uncertainty intervals were minimum and the modes, means and medians of the posteriors were similar to the estimates using the existing classical methods. From the comparitive analysis between Bayesian and plug-in spatial predictions, we could find that the uncertainty of plug-in prediction could be slightly underestimated than that of Bayesian prediction.
Keywords
Probability rainfall; Spatial distribution; Parameter uncertainty; Spatial linear mixed model; Bayesian inference;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Diggle, P. J., Ribeiro Jr, P. J., 2007, Model-based geostatistics, Springer, New York.
2 Diggle, P. J., Tawn, J., Moyeed, R., 1998, Model based geostatistics (with discussion), Journal of the Royal Statistical Society, Series C, 47(3), 299-350.
3 Ecker, M., Gelfand, A., 1997, Bayesian variogram modeling for an isotropic spatial process, Journal of Agricultural, Biological, and Environmental Statistics, 2(4), 347-369.   DOI
4 Handcock, M., Stein, M., 1993, A Bayesian analysis of kriging, Technometrics, 35(4), 403-410.   DOI
5 Le, N., Zidek, J., 1992, Interpolation with uncertain spatial covariance: a Bayesian alternative to kriging, Journal of Multivariate Analysis, 43(375), 351-374.   DOI
6 R Development Core Team, 2011, R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http:// www.R-project.org.
7 Ribeiro Jr, P. J., Diggle, P. J., 1999, Bayesian inference in Gaussian model-based geostatistics, Technical Report ST-99-08, Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.
8 서영민, 여운기, 이승윤, 지홍기, 2010a, 확률강우량의 공간분포 추정을 위한 KED 기법의 적용, 한국수자원학회논문집, 43(8), 757-767.   과학기술학회마을   DOI
9 서영민, 여운기, 이승윤, 지홍기, 2010b, 확률강우량의 공간분포에 대한 불확실성 해석 - CEM과 SGS 기법의 비교, 한국수자원학회논문집, 43(11), 933-944.   과학기술학회마을   DOI
10 Berger, J., De Oliveira, V., Sanso, B., 2001, Objective Bayesian analysis of spatially correlated data, Journal of the American Statistical Association, 96, 1361-1374.   DOI
11 Diggle, P. J., Ribeiro Jr, P. J., 2002, Bayesian inference in Gaussian model-based geo-statistics, Geographical and Environmental Modelling, 6(2), 129-146.   DOI
12 Cressie, N., 1993, Statistics for spatial data, Wiley, New York.
13 De Oliveira, V., Kedem, B., Short, D., 1997, Bayesian prediction of transformed Gaussian random fields, Journal of the American Statistical Association, 92(440), 1422-1433.   DOI