Multiple Change-Point Estimation of Air Pollution Mean Vectors

- Journal title : Korean Journal of Applied Statistics
- Volume 22, Issue 4, 2009, pp.687-695
- Publisher : The Korean Statistical Society
- DOI : 10.5351/KJAS.2009.22.4.687

Title & Authors

Multiple Change-Point Estimation of Air Pollution Mean Vectors

Kim, Jae-Hee; Cheon, Sooy-Oung;

Kim, Jae-Hee; Cheon, Sooy-Oung;

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

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

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

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

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

5.

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

6.

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

7.

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

8.

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

9.

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

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

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

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