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Multiple Change-Point Estimation of Air Pollution Mean Vectors

  • Kim, Jae-Hee (Department of Statistics, Duksung Women's University) ;
  • Cheon, Sooy-Oung (KU Industry-Academy Cooperation Group Team of Economics and Statistics, Korea University)
  • Published : 2009.08.31

Abstract

The Bayesian multiple change-point estimation has been applied to the daily means of ozone and PM10 data in Seoul for the period 1999. We focus on the detection of multiple change-points in the ozone and PM10 bivariate vectors by evaluating the posterior probabilities and Bayesian information criterion(BIC) using the stochastic approximation Monte Carlo(SAMC) algorithm. The result gives 5 change-points of mean vectors of ozone and PM10, which are related with the seasonal characteristics.

Keywords

References

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