The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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A nonparametric Bayesian seemingly unrelated regression model

비모수 베이지안 겉보기 무관 회귀모형

  • Jo, Seongil (Department of Statistics, Korea University) ;
  • Seok, Inhae (Department of Statistics, Korea University) ;
  • Choi, Taeryon (Department of Statistics, Korea University)
  • Received : 2016.02.24
  • Accepted : 2016.04.25
  • Published : 2016.06.30
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Abstract

In this paper, we consider a seemingly unrelated regression (SUR) model and propose a nonparametric Bayesian approach to SUR with a Dirichlet process mixture of normals for modeling an unknown error distribution. Posterior distributions are derived based on the proposed model, and the posterior inference is performed via Markov chain Monte Carlo methods based on the collapsed Gibbs sampler of a Dirichlet process mixture model. We present a simulation study to assess the performance of the model. We also apply the model to precipitation data over South Korea.

본 논문에서는 겉보기 무관 회귀모형을 고려하고 디리크레 프로세스 혼합모형을 오차항의 분포로 하는 비모수 베이지안 방법을 제안한다. 제안된 모형을 바탕으로 사후분포를 유도하고 디리크레 프로세스 혼합모형의 붕괴깁스표집 방법을 통해 마코프 체인 몬테 칼로 알고리듬을 구성하고 사후추론을 실시한다. 모형의 성능을 비교하기 위해 모의실험을 실시하고, 더 나아가 한국지역의 강수량 예측에 대한 실제 자료에 적용해 본다.

Keywords

References

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