The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Bayesian Detection of Multiple Change Points in a Piecewise Linear Function

구분적 선형함수에서의 베이지안 변화점 추출

  • Kim, Joungyoun (Biostatistics and Clinical Epidemiology Center, Research Institute for Future Medicine, Samsung Medical Center)
  • 김정연 (삼성 서울병원, 의생명 정보센터)
  • Received : 2014.04.30
  • Accepted : 2014.08.14
  • Published : 2014.08.31
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Abstract

When consecutive data follows different distributions(depending on the time interval) change-point detection infers where the changes occur first and then finds further inferences for each sub-interval. In this paper, we investigate the Bayesian detection of multiple change points. Utilizing the reversible jump MCMC, we can explore parameter spaces with unknown dimensions. In particular, we consider a model where the signal is a piecewise linear function. For the Bayesian inference, we propose a new Bayesian structure and build our own MCMC algorithm. Through the simulation study and the real data analysis, we verified the performance of our method.

본 연구는 시간의 순서에 따라 순차적으로 발생한 신호 자료에 있어서, 변화점 검출을 위한 베이지안 방법을 개발하고자 한다. 특히, Reversible Jump MCMC를 이용하여, 차원이 정해지지 않은 모수 공간을 탐색할 수 있는 효율적인 베이지안 추론 모형을 개발한다. 신호가 각 구간에서 선형함수인 경우에 대한 모형과 이해가 용이한 모형을 제안하고, 추정을 위해 고유의 MCMC알고리즘을 개발하였다. 제안된 방법을 모의실험 자료에 적용함으로써 그 정확성 및 효율성을 검증하였고, 실제 자료에도 적용하여 보았다.

Keywords

References

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