Communications for Statistical Applications and Methods

  • 제19권2호
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  • Pages.237-246
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  • 2012
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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소량자료를 위한 베이지안 다중 변환점 모형

Bayesian Multiple Change-Point for Small Data

  • 전수영 (고려대학교 정보통계학과) ;
  • Cheon, Soo-Young (Department of Informational Statistics, Korea University) ;
  • Yu, Wenxing (Department of Economics and Statistics, Korea University)
  • 투고 : 2011.12.16
  • 심사 : 2012.01.20
  • 발행 : 2012.03.31
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⟨ 이전 논문 다음 논문 ⟩

초록

다중 변환점(multiple change-point) 추론에 있어 소량자료에 관한 연구는 많지 않다. 본 논문에서는 소량 자료의 다중 변환점 추정을 위해 베이지안 비중심(noncentral) t 분포 변환점 모형을 제안하고, 제안된 모형 추론을 위해 메트로폴리스-해스팅스를 포함한 깁스 샘플링(Metropolis-Hastings-Within-Gibbs sampling) 알고리즘을 이용하였다. 모의실험 및 태풍 발생 수의 실증 분석결과는 제안된 모형과 알고리즘의 우수성을 보여 준다.

Bayesian methods have been recently used to identify multiple change-points. However, the studies for small data are limited. This paper suggests the Bayesian noncentral t distribution change-point model for small data, and applies the Metropolis-Hastings-within-Gibbs Sampling algorithm to the proposed model. Numerical results of simulation and real data show the performance of the new model in terms of the quality of the resulting estimation of the numbers and positions of change-points for small data.

키워드

참고문헌

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피인용 문헌

  1. Bayesian Multiple Change-Point Estimation of Multivariate Mean Vectors for Small Data vol.25, pp.6, 2012, https://doi.org/10.5351/KJAS.2012.25.6.999