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Space-Time Data fusion Under Error in Computer Model Output: An Application to Modeling Air Quality

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  • Veronica J. Berrocal
  • Alan E. Gelfand
  • David M. Holland

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  • Veronica J. Berrocal & Alan E. Gelfand & David M. Holland, 2012. "Space-Time Data fusion Under Error in Computer Model Output: An Application to Modeling Air Quality," Biometrics, The International Biometric Society, vol. 68(3), pages 837-848, September.
    • Handle: RePEc:bla:biomet:v:68:y:2012:i:3:p:837-848
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2011.01725.x
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    References listed on IDEAS

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    1. Zhang, Hao, 2004. "Inconsistent Estimation and Asymptotically Equal Interpolations in Model-Based Geostatistics," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 250-261, January.
    2. Sudipto Banerjee & Alan E. Gelfand & Andrew O. Finley & Huiyan Sang, 2008. "Gaussian predictive process models for large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 825-848, September.
    3. Alan Gelfand & Alexandra Schmidt & Sudipto Banerjee & C. Sirmans, 2004. "Nonstationary multivariate process modeling through spatially varying coregionalization," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 13(2), pages 263-312, December.
    4. Montserrat Fuentes & Adrian E. Raftery, 2005. "Model Evaluation and Spatial Interpolation by Bayesian Combination of Observations with Outputs from Numerical Models," Biometrics, The International Biometric Society, vol. 61(1), pages 36-45, March.
    5. Sahu, Sujit K. & Gelfand, Alan E. & Holland, David M., 2007. "High-Resolution SpaceTime Ozone Modeling for Assessing Trends," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1221-1234, December.
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    Cited by:

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    2. C. Forlani & S. Bhatt & M. Cameletti & E. Krainski & M. Blangiardo, 2020. "A joint Bayesian space–time model to integrate spatially misaligned air pollution data in R‐INLA," Environmetrics, John Wiley & Sons, Ltd., vol. 31(8), December.
    3. Brian J. Reich & Howard H. Chang & Kristen M. Foley, 2014. "A spectral method for spatial downscaling," Biometrics, The International Biometric Society, vol. 70(4), pages 932-942, December.
    4. Chen, Yewen & Chang, Xiaohui & Luo, Fangzhi & Huang, Hui, 2023. "Additive dynamic models for correcting numerical model outputs," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    5. Nathan A. Ryder & Joshua P. Keller, 2023. "Spatiotemporal Exposure Prediction with Penalized Regression," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(2), pages 260-278, June.
    6. Finazzi, Francesco & Fassò, Alessandro, 2014. "D-STEM: A Software for the Analysis and Mapping of Environmental Space-Time Variables," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i06).
    7. Eum, Youngseob & Yoo, EunHye & Bowen, Elizabeth, 2019. "Socioeconomic determinants of pediatric asthma emergency department visits under regional economic development in western New York," Social Science & Medicine, Elsevier, vol. 222(C), pages 133-144.
    8. Jiafang Song & Joshua L. Warren, 2022. "A Directionally Varying Change Points Model for Quantifying the Impact of a Point Source," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(1), pages 46-62, March.
    9. Daisuke Murakami & Morito Tsutsumi, 2015. "Area-to-point parameter estimation with geographically weighted regression," Journal of Geographical Systems, Springer, vol. 17(3), pages 207-225, July.
    10. Grineski, Sara & Collins, Tim & Renteria, Roger & Rubio, Ricardo, 2021. "Multigenerational immigrant trajectories and children's unequal exposure to fine particulate matter in the US," Social Science & Medicine, Elsevier, vol. 282(C).
    11. Hang Zhang & Yong Liu & Dongyang Yang & Guanpeng Dong, 2022. "PM 2.5 Concentrations Variability in North China Explored with a Multi-Scale Spatial Random Effect Model," IJERPH, MDPI, vol. 19(17), pages 1-14, August.
    12. Casey Mullen & Sara E. Grineski & Timothy W. Collins & Daniel L. Mendoza, 2020. "Effects of PM 2.5 on Third Grade Students’ Proficiency in Math and English Language Arts," IJERPH, MDPI, vol. 17(18), pages 1-21, September.
    13. Amy J. Schulz & Graciela B. Mentz & Natalie Sampson & Melanie Ward & J. Timothy Dvonch & Ricardo De Majo & Barbara A. Israel & Angela G. Reyes & Donele Wilkins, 2018. "Independent and Joint Contributions of Fine Particulate Matter Exposure and Population Vulnerability to Mortality in the Detroit Metropolitan Area," IJERPH, MDPI, vol. 15(6), pages 1-15, June.
    14. Niru Senthilkumar & Mark Gilfether & Francesca Metcalf & Armistead G. Russell & James A. Mulholland & Howard H. Chang, 2019. "Application of a Fusion Method for Gas and Particle Air Pollutants between Observational Data and Chemical Transport Model Simulations Over the Contiguous United States for 2005–2014," IJERPH, MDPI, vol. 16(18), pages 1-15, September.

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