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Spatial Prediction Based on the Bayesian Kriging with Box-Cox Transformation
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 Title & Authors
Spatial Prediction Based on the Bayesian Kriging with Box-Cox Transformation
Choi, Jung-Soon; Park, Man-Sik;
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 Abstract
In the last decades, there has been much interest in climate variability because its change has dramatic effects on humanity. Especially, the precipitation data are measured over space and their spatial association is so complicated. So we should take into account such a spatial dependency structure while analyzing the data. However, in linear models for analyzing the data, data sets show severely skewed distribution. In the paper, we consider the Box-Cox transformation to satisfy the normal distribution prior to the analysis, and employ a Bayesian hierarchical framework to investigate the spatial patterns. The data set we considered is monthly average precipitation of the third quarter of 2007 obtained from 347 automated monitoring stations in Contiguous South Korea.
 Keywords
Precipitation;Bayesian kriging;Box-Cox transformation;
 Language
English
 Cited by
1.
다양한 관측네트워크에서 얻은 공간자료들을 활용한 계층모형 구축,최지은;박만식;

응용통계연구, 2013. vol.26. 1, pp.93-109 crossref(new window)
1.
On the Hierarchical Modeling of Spatial Measurements from Different Station Networks, Korean Journal of Applied Statistics, 2013, 26, 1, 93  crossref(new windwow)
 References
1.
Banerjee, S., Carlin, B. P. and Gelfand, A. E. (2004). Hierarchical Modeling and Analysis for Spatial Data, Chapman & Hall/CRC, Florida

2.
Box, G. E. P. and Cox, D. R. (1964). An analysis of transformations, Journal of the Royal Statistical Society, Series B, 26, 211-246

3.
Brown, P. J., Le, N. D. and Zidek, J. V. (1994). Multivariate spatial interpolation and exposure to air pollutants, Canadian Journal of Statistics, 22, 489-509 crossref(new window)

4.
Cressie, N., Frey, J., Harch, B. and Smith, M. (2006). Spatial prediction on a river network, Journal of Agricultural, Biological, and Environmental Statistics, 11, 127-150 crossref(new window)

5.
De Oliveira, V., Kedem, B, and Short, D. A. (1997). Bayesian prediction of transformed Gaussian random fields, Journal of the American Statistical Association, 92, 1422-1433 crossref(new window)

6.
Diggle, P. J., Tawn, J. A. and Moyeed, R. A. (1998). Model-based geostatistics (with discussion), Applied Statistics, 47, 299-350 crossref(new window)

7.
Ecker, M. D. and Gelfand, A. E. (1997). Bayesian variogram modeling for an isotropic spatial process, Journal of Agricultural, Biological, and Environmental Statistics, 2, 347-369 crossref(new window)

8.
Finley, A. O., Banerjee, S. and Carlin, B. P. (2008). spBayes: Univariate and Multivariate Spatial Modeling, R package version 0.1-0

9.
Handcock, M. S. and Stein, M. L. (1993). A Bayesian analysis of kriging, Technometrics, 35, 403-410 crossref(new window)

10.
Handcock, M. S. and Wallis, J. R. (1994). An approach to statistical spatio-temporal modeling of meteorological fields, Journal of the American Statistical Association, 89, 368-378 crossref(new window)

11.
Heo, T. Y. and Park, M. S. (2009). Bayesian spatial modeling of precipitation data, Korean Journal of Applied Statistics, 22, 425-433 crossref(new window)

12.
Karson, M. J., Gaudard, M., Linder, E. and Sinha, D. (1999). Bayesian analysis and computations for spatial prediction (with discussion), Environmental and Ecological Statistics, 6, 147-182 crossref(new window)

13.
Le, N. D. and Zidek, J. V. (1992). Interpolation with uncertain spatial covariance: A Bayesian alter-native to Kriging, Journal of Multivariate Analysis, 43, 351-374 crossref(new window)

14.
Park, M. S. and Heo, T. Y. (2009). Seasonal spatial-temporal model for rainfall data of South Korea, Journal of Applied Sciences Research, 5, 565-572

15.
R Development Core Team (2008). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Vienna, Austria, ISBN 3-900051-07-0

16.
Ribeiro, P. J. and Diggle, P. J. (2001). geoR: A package for geostatistical analysis, R-NEWS, 1, 15-18

17.
Roberts, G. O. (1996). Markov Chain Concepts Related to Sampling Algorithms, in Markov Chain Monte Carlo in Practice, edited by W. R. Gilks, S. Richardson and D. J. Spiegeihalter. Chapman & Hall/CRC, London, 45-57

18.
Schabenberger, O. and Gotway, C. A. (2004). Statistical Methods for Spatial Data Analysis, Chapman & Hall/CRC, Florida

19.
Smith, R. L., Kolenikov, S. and Cox, L. H. (2003). Spatiotemporal modeling of PM2.5 data with missing values, Journal of Geophysical Research, 108, STS 11-1