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Bayesian Multiple Change-Point for Small Data

소량자료를 위한 베이지안 다중 변환점 모형

  • Cheon, Soo-Young (Department of Informational Statistics, Korea University) ;
  • Yu, Wenxing (Department of Economics and Statistics, Korea University)
  • 전수영 (고려대학교 정보통계학과) ;
  • Received : 2011.12.16
  • Accepted : 2012.01.20
  • Published : 2012.03.31

Abstract

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.

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Cited by

  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