The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Principal component regression for spatial data

공간자료 주성분분석

  • Lim, Yaeji (Department of Statistics, Pukyong National University)
  • Received : 2016.11.23
  • Accepted : 2017.02.04
  • Published : 2017.06.30
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Abstract

Principal component analysis is a popular statistical method to reduce the dimension of the high dimensional climate data and to extract meaningful climate patterns. Based on the principal component analysis, we can further apply a regression approach for the linear prediction of future climate, termed as principal component regression (PCR). In this paper, we develop a new PCR method based on the regularized principal component analysis for spatial data proposed by Wang and Huang (2016) to account spatial feature of the climate data. We apply the proposed method to temperature prediction in the East Asia region and compare the result with conventional PCR results.

주성분 분석은 통계학 뿐만 아니라 기상학에서 널리 사용되는 방법론이며, 고차원 자료에 대한 차원축소 역할 뿐만아니라 기상자료에서의 의미있는 패턴을 찾아내기 위해 사용되는 방법론이다. 또한 주성분분석에 기반을 둔 주성분 회귀분석 방법론은 기후예측이 가능하므로 미래 시점의 기후값 예측에 사용될 수 있다. 본 논문에서는 Wang과 Huang (2016) 논문에서 제안한 제한된 공간 주성분 분석을 기반으로 한 주성분 회귀분석 방법론을 개발하였다. 이를 시뮬레이션을 통하여 확인하였고, 실제 자료인 동아시아 지역 온도예측에 적용하여 기존의 주성분 회귀분석 예측 값에 비해 예측력이 높아짐을 확인하였다.

Keywords

Acknowledgement

Supported by : 부경대학교, 한국연구재단

References

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