JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Multiple Change-Point Estimation of Air Pollution Mean Vectors
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Multiple Change-Point Estimation of Air Pollution Mean Vectors
Kim, Jae-Hee; Cheon, Sooy-Oung;
  PDF(new window)
 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
Bayesian change-point model;Bayesian information criterion(BIC);multivariate normal distribution;ozone;PM10;posterior;stochastic approximation Monte Carlo(SAMC);truncated Poisson;
 Language
English
 Cited by
 References
1.
Barry, D. and Hartigan, J. A. (1993). A Bayesian analysis for change point problems, Journal of the American Statistical Association, 88, 309-319 crossref(new window)

2.
Borchi, F., Naveau, P., Keckhut, P. and Hauchecorne, A. (2006). Detecting variability changes in Arctic total ozone column, Journal of Atmoshperic and Solar-Terrestrail Physics, 68, 1383-1395 crossref(new window)

3.
Carlin, B. P., Gelfand, A. E. and Smith, A. F. M. (1992). Hierarchical Bayesian analysis of change point problems, Journal of the Royal Statistical Society. Series C(Applied Statistics), 41, 389-405 crossref(new window)

4.
Carslaw, D., Ropkins, K. and Bell, M. C. (2006). Change-point detection of gaseous and particulate traffic-related pollutants at a roadside location, Environmental Science & Technology, 40, 6912-2918 crossref(new window)

5.
Cheon, S. and Kim, J. (2009). Multiple Change-point detection of multivariate mean vectors with Bayesian approach, Computational Statistics & Data Analysis, in revision crossref(new window)

6.
Crowley, E. M. (1997). Product partition models for normal means, Journal of the American Statistical Association, 92, 192-198 crossref(new window)

7.
Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination, Biometrika, 82, 711-732 crossref(new window)

8.
Jaruskova, D. (1997). Some problems with application of change-point detection methods to evnironmental data, Environmetrics, 8, 469-483 crossref(new window)

9.
Liang, F. (2009). Improving SAMC using smoothing methods: Theory and applications to Bayesian model selection problems, The Annals of Statistics, in press crossref(new window)

10.
Liang, F., Liu, C. and Carroll, R. J. (2007). Stochastic approximation in Monte Carlo computation, Journal of the American Statistical Association, 102, 305-320 crossref(new window)

11.
Loschi, R. H. and Cruz, F. R. B. (2005). Extension to the product partition model: Computing the probability of a change, Computational Statistics & Data Analysis, 48, 255-268 crossref(new window)

12.
Safadi, T. and Pena, D. (2008). Bayesian analysis of dynamic factor models: An application to air pollution and mortality in Sao Paulo, Brazil, Environmetrics, 19, 582-601 crossref(new window)

13.
Smith, A. F. M. (1975). A Bayesian approach to inference about a change-point in a sequence of random variables, Biometrika, 62, 407-416 crossref(new window)

14.
Son, Y. S. and Kim, S. W. (2005). Bayesian single change point detection in a sequence of multivariate normal observations, Statistics, 39, 373-387 crossref(new window)