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Spatial variable selection methods for investigating acute health effects of fine particulate matter components

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  • Laura F. Boehm Vock
  • Brian J. Reich
  • Montserrat Fuentes
  • Francesca Dominici

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  • Laura F. Boehm Vock & Brian J. Reich & Montserrat Fuentes & Francesca Dominici, 2015. "Spatial variable selection methods for investigating acute health effects of fine particulate matter components," Biometrics, The International Biometric Society, vol. 71(1), pages 167-177, March.
    • Handle: RePEc:bla:biomet:v:71:y:2015:i:1:p:167-177
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    File URL: http://hdl.handle.net/10.1111/biom.12254
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    References listed on IDEAS

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    1. Brian J. Reich & Montserrat Fuentes & Amy H. Herring & Kelly R. Evenson, 2010. "Bayesian Variable Selection for Multivariate Spatially Varying Coefficient Regression," Biometrics, The International Biometric Society, vol. 66(3), pages 772-782, September.
    2. Smith, Michael & Fahrmeir, Ludwig, 2007. "Spatial Bayesian Variable Selection With Application to Functional Magnetic Resonance Imaging," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 417-431, June.
    3. Choi, Jungsoon & Fuentes, Montserrat & Reich, Brian J., 2009. "Spatial-temporal association between fine particulate matter and daily mortality," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2989-3000, June.
    4. Montserrat Fuentes & Hae-Ryoung Song & Sujit K. Ghosh & David M. Holland & Jerry M. Davis, 2006. "Spatial Association between Speciated Fine Particles and Mortality," Biometrics, The International Biometric Society, vol. 62(3), pages 855-863, September.
    5. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    6. Ida Scheel & Egil Ferkingstad & Arnoldo Frigessi & Ola Haug & Mikkel Hinnerichsen & Elisabeth Meze-Hausken, 2013. "A Bayesian hierarchical model with spatial variable selection: the effect of weather on insurance claims," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(1), pages 85-100, January.
    7. Francesca Dominici & Jonathan M. Samet & Scott L. Zeger, 2000. "Combining evidence on air pollution and daily mortality from the 20 largest US cities: a hierarchical modelling strategy," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(3), pages 263-302.
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    Cited by:

    1. Hu, Guanyu, 2021. "Spatially varying sparsity in dynamic regression models," Econometrics and Statistics, Elsevier, vol. 17(C), pages 23-34.
    2. Faizeh Hatami & Shi Chen & Rajib Paul & Jean-Claude Thill, 2022. "Simulating and Forecasting the COVID-19 Spread in a U.S. Metropolitan Region with a Spatial SEIR Model," IJERPH, MDPI, vol. 19(23), pages 1-16, November.
    3. Guanyu Hu & Yishu Xue & Fred Huffer, 2021. "A Comparison of Bayesian Accelerated Failure Time Models with Spatially Varying Coefficients," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 541-557, November.
    4. Zhihua Ma & Yishu Xue & Guanyu Hu, 2021. "Geographically Weighted Regression Analysis for Spatial Economics Data: A Bayesian Recourse," International Regional Science Review, , vol. 44(5), pages 582-604, September.

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