Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Bayes Inference for the Spatial Time Series Model

공간시계열모형에 대한 베이즈 추론

  • Lee, Sung-Duck (Dept. of Information & Statistics, Chungbuk Univ.) ;
  • Kim, In-Kyu (Div. of Computer Information & Communication of Information, Woosong Info. Col.) ;
  • Kim, Duk-Ki (Dept. of Information & Statistics, Chungbuk Univ.) ;
  • Chung, Ae-Ran (Dept. of Information & Statistics, Chungbuk Univ.)
  • 이성덕 (충북대학교 정보통계학과) ;
  • 김인규 (우송 정보대학교 컴퓨터정보통신계열) ;
  • 김덕기 (충북대학교 정보통계학과) ;
  • 정애란 (충북대학교 정보통계학과)
  • Published : 2009.01.31
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Abstract

Spatial time series data can be viewed either as a set of time series collected simultaneously at a number of spatial locations. In this paper, We estimate the parameters of spatial time autoregressive moving average (SIARMA) process by method of Gibbs sampling. Finally, We apply this method to a set of U.S. Mumps data over a 12 states region.

공간시계열모형은 공간의 위치와 시간의 흐름에 따라 동시에 관측되는 분야인 기상, 지질, 천문, 생태, 역학 등에서 넓이 사용되고 있는 매우 복잡한 모형이다. 본 논문은 공간시계열모형에 대한 모수 추정에 있어서 기존의 최대우도추정 방법이 가지는 컴퓨팅의 문제를 해결하기 위하여 모수에 대한 사전정보와 자료의 정보를 모두 이용하는 깁스샘플링과 같은 MCMC 방법으로 모수를 추정하고, 실제 적용사례분석으로 여러 가지 측도를 구해서 추정된 모수에 대한 수렴진단을 수행하였다.

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References

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