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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2015.4
- 수록면
- 495 - 504 (10page)
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The Gaussian geostatistical model has been widely used for modeling spatial data. However, this model suffers from a severe dificulty in computation because inference requires to invert a large covariance matrix in evaluating log-likelihood. In addressing this computational challenge, three strategies have been employed: likelihood approx-imation, lower dimensional space approximation, and Markov random field approximation. In this paper, we reviewed statistical approaches attacking the computational challenge. As an illustration, we also applied integrated nested Laplace approximation (INLA) technology, one of Markov approximation approach, to real data to provide an example of its use in practice dealing with large spatial data.
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참고문헌 (24)
참고문헌 신청
1. [학술지(정기간행물)] - Banerjee, S. / 2008 / Gaussian predictive process models for large spatial data sets / Journal of the Royal Statistical Society B 70 : 825 ~ 848
2. [학술지(정기간행물)] - Besag, J. / 2005 / First-order intrinsic autoregressions and the Wijs process / Biometrika 92 : 909 ~ 920
3. [학술지(정기간행물)] - Blangiardo, M. / 2013 / Spatial and spatio-temporal models with R-INLA / Spatial and Spatio-Temporal Epidemiology 4 : 33 ~ 49
4. [단행본] - Cressie, N. A. C. / 1993 / Statistics for spatial data / Wiley
5. [학술지(정기간행물)] - Fuentes, M. / 2007 / Approximate likelihood for large irregularly spaced spatial data / Journal of the Amer-ican Statistical Association 102 : 321 ~ 331
2. [학술지(정기간행물)] - Besag, J. / 2005 / First-order intrinsic autoregressions and the Wijs process / Biometrika 92 : 909 ~ 920
3. [학술지(정기간행물)] - Blangiardo, M. / 2013 / Spatial and spatio-temporal models with R-INLA / Spatial and Spatio-Temporal Epidemiology 4 : 33 ~ 49
4. [단행본] - Cressie, N. A. C. / 1993 / Statistics for spatial data / Wiley
5. [학술지(정기간행물)] - Fuentes, M. / 2007 / Approximate likelihood for large irregularly spaced spatial data / Journal of the Amer-ican Statistical Association 102 : 321 ~ 331
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UCI(KEPA) : I410-ECN-0101-2018-041-001376370