Analysis of air quality time series of Hong Kong with graphical modeling

Environmetrics

Volumn 27, Issue 3, 2016, Pages 169-181

  Hu, Fei ^a,b   Lu, Zudi ^c   Wong, Heung ^d   Yuen, Tsz P ^d  

^a TIANJIN UNIVERSITY   (China)

^b CITY UNIVERSITY OF HONG KONG   (Hong Kong)

^c UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

^d HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

Air pollutants in Hong Kong; Air quality monitoring stations; Graphical model; Partial coherence; VAR model

Indexed keywords

AIR QUALITY; ATMOSPHERE; ATMOSPHERIC POLLUTION; DATA ACQUISITION; ENVIRONMENTAL MANAGEMENT; ENVIRONMENTAL MODELING; ESTIMATION METHOD; GRAPHICAL METHOD; POLLUTION MONITORING; SPATIOTEMPORAL ANALYSIS; TIME SERIES ANALYSIS;

CHINA; HONG KONG; NEW TERRITORIES; TSUEN WAN;

EID: 84959499555     PISSN: 11804009     EISSN: 1099095X     Source Type: Journal    
DOI: 10.1002/env.2386     Document Type: Article

References (20)

1. Air Science Group, Environmental Protection Department, the Government of the Hong Kong Special Administrative Region . 2012. Air quality in Hong Kong 2012. http://www.aqhi.gov.hk/api_history/english/report/files/AQR2012e_ final.pdf [accessed 16 December 2015].
2. Brillinger DR. 1981. Time Series: Data Analysis and Theory, Expanded. Holden-Day: San Francisco.
3. Brillinger DR. 1996. Remarks concerning graphical models for time series and point processes. Revista de Econometria 16: 1-23.
4. Box GEP, Cox DR. 1964. An analysis of transformations. Journal of the Royal Statistical Society: Series B 26(2): 211-252.
5. Cleveland WS. 1979. Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association 74(368): 829-836.
6. Dahlhaus R. 2000. Graphical interaction models for multivariate time series. Metrika 51(2): 157-172.
7. Dahlhaus R, Eichler M, Sandkühler J. 1997. Identification of synaptic connections in neural ensembles by graphical models. Journal of Neuroscience Methods 77(1): 93-107.
8. Edwards D.. 2000. Introduction to Graphical Modelling. Springer: New York.
9. Eichler M. 2012. Graphical modelling of multivariate time series. Probability Theory and Related Fields 153: 233-268.
10. Forni M, Hallin M, Lippi M, Reichlin L. 2000. The generalized dynamic-factor model: identification and estimation. The Review of Economics and Statistics 82(4): 540-554.
11. Hastie T, Tibshirani R, Friedman J. 2013. The Elements of Statistical Learning, 3. Springer: New York.
12. Jung A, Hannak G, Goertz N. 2015. Graphical LASSO based model selection for time series. Signal Processing Letters, IEEE 22(10): 1781-1785.
13. Khare K, Oh S-Y, Rajaratnam B. 2015. A convex pseudolikelihood framework for high dimensional partial correlation estimation with convergence guarantees. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 77(4): 803-825.
14. Koopmans L. H.. 1995. The Spectral Analysis of Time Series. New York: Academic Press.
15. Lauritzen SL. 1996. Graphical Models. Clarendon Press: Oxford England.
16. McLeod AI, Gweon H. 2012. Optimal deseasonalization for monthly and daily geophysical time series. Journal of Environmental Statistics 4(11).
17. Priestley MB. 1981. Spectral Analysis and Time Series, Vol. 1 and 2. Academic Press: London.
18. Qiu H, Han F, Liu H, Caffo B. 2015. Joint estimation of multiple graphical models from high dimensional time series. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 78(2): 487-504.
19. Tunnicliffe Wilson Granville, Reale Macro, Haywood John. 2015. Models for Dependent Time Series. CRC Press: Boca Raton.
20. Wolstenholme RJ, Walden AT. 2015. An efficient approach to graphical modeling of time series. Signal Processing, IEEE Transactions on 63(12): 3266-3276.