Communications for Statistical Applications and Methods

  • 제16권5호
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  • Pages.851-858
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  • 2009
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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Spatial Prediction Based on the Bayesian Kriging with Box-Cox Transformation

  • 발행 : 2009.09.30
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초록

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.

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참고문헌

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피인용 문헌

  1. On the Hierarchical Modeling of Spatial Measurements from Different Station Networks vol.26, pp.1, 2013, https://doi.org/10.5351/KJAS.2013.26.1.093