Temporal Forecasting with a Bayesian Spatial Predictor: Application to Ozone
This paper develops and empirically compares two Bayesian and empirical Bayes space-time approaches for forecasting next-day hourly ground-level ozone concentrations. The comparison involves the Chicago area in the summer of 2000 and measurements from fourteen monitors as reported in the EPA's...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2012-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2012/191575 |
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| _version_ | 1849398703838724096 |
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| author | Yiping Dou Nhu D. Le James V. Zidek |
| author_facet | Yiping Dou Nhu D. Le James V. Zidek |
| author_sort | Yiping Dou |
| collection | DOAJ |
| description | This paper develops and empirically compares two Bayesian and empirical Bayes space-time approaches for forecasting next-day hourly ground-level ozone concentrations. The comparison involves the Chicago area in the summer of 2000 and measurements from fourteen monitors as reported in the EPA's AQS database. One of these approaches adapts a multivariate method originally designed for spatial prediction. The second is based on a state-space modeling approach originally developed and used in a case study involving one week in Mexico City with ten monitoring sites. The first method proves superior to the second in the Chicago Case Study, judged by several criteria, notably root mean square predictive accuracy, computing times, and calibration of 95% predictive intervals. |
| format | Article |
| id | doaj-art-9be8a5cc89bb4ddeaba703097939d666 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-9be8a5cc89bb4ddeaba703097939d6662025-08-20T03:38:31ZengWileyAdvances in Meteorology1687-93091687-93172012-01-01201210.1155/2012/191575191575Temporal Forecasting with a Bayesian Spatial Predictor: Application to OzoneYiping Dou0Nhu D. Le1James V. Zidek2Finance, eBay Inc., San Jose, CA 95125, USABC Cancer Agency Research Center, Vancouver, BC, V5Z 4E6, CanadaDepartment of Statistics, University of British Columbia, Vancouver, BC, V6T 1Z2, CanadaThis paper develops and empirically compares two Bayesian and empirical Bayes space-time approaches for forecasting next-day hourly ground-level ozone concentrations. The comparison involves the Chicago area in the summer of 2000 and measurements from fourteen monitors as reported in the EPA's AQS database. One of these approaches adapts a multivariate method originally designed for spatial prediction. The second is based on a state-space modeling approach originally developed and used in a case study involving one week in Mexico City with ten monitoring sites. The first method proves superior to the second in the Chicago Case Study, judged by several criteria, notably root mean square predictive accuracy, computing times, and calibration of 95% predictive intervals.http://dx.doi.org/10.1155/2012/191575 |
| spellingShingle | Yiping Dou Nhu D. Le James V. Zidek Temporal Forecasting with a Bayesian Spatial Predictor: Application to Ozone Advances in Meteorology |
| title | Temporal Forecasting with a Bayesian Spatial Predictor: Application to Ozone |
| title_full | Temporal Forecasting with a Bayesian Spatial Predictor: Application to Ozone |
| title_fullStr | Temporal Forecasting with a Bayesian Spatial Predictor: Application to Ozone |
| title_full_unstemmed | Temporal Forecasting with a Bayesian Spatial Predictor: Application to Ozone |
| title_short | Temporal Forecasting with a Bayesian Spatial Predictor: Application to Ozone |
| title_sort | temporal forecasting with a bayesian spatial predictor application to ozone |
| url | http://dx.doi.org/10.1155/2012/191575 |
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