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Spatiotemporal smoothing and sulphur dioxide trends over Europe

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  • Adrian W. Bowman
  • Marco Giannitrapani
  • E. Marian Scott

Abstract

Summary. Spatiotemporal models for sulphur dioxide pollution over Europe are considered within an additive model framework. A suitable description of the spatiotemporal correlation structure of the data is constructed and incorporated in the analysis of the additive model, to ensure that standard errors and other forms of analysis reflect the form of variation that is exhibited by the data. To deal with the large sample size, an updating formula based on binning is derived to provide a computationally manageable implementation of the back‐fitting algorithm. Interaction terms involving space, time and seasonal effects are also considered. This requires three‐dimensional smoothing which is implemented by repeated application of lower dimensional marginal smoothing operations. The properties of this form of smoothing are examined and the estimators are shown to have first‐order behaviour, inherited from the marginal operations, which is equivalent to the full multivariate versions. These models and methods are applied to the sulphur dioxide data, allowing detailed and informative descriptions of the spatiotemporal patterns to be created.

Suggested Citation

  • Adrian W. Bowman & Marco Giannitrapani & E. Marian Scott, 2009. "Spatiotemporal smoothing and sulphur dioxide trends over Europe," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(5), pages 737-752, December.
    • Handle: RePEc:bla:jorssc:v:58:y:2009:i:5:p:737-752
    DOI: 10.1111/j.1467-9876.2009.00671.x
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    References listed on IDEAS

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    1. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, June.
    2. I. D. Currie & M. Durban & P. H. C. Eilers, 2006. "Generalized linear array models with applications to multidimensional smoothing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(2), pages 259-280, April.
    3. Noel Cressie & Gardar Johannesson, 2008. "Fixed rank kriging for very large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 209-226, February.
    4. Marx, Brian D. & Eilers, Paul H. C., 1998. "Direct generalized additive modeling with penalized likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 28(2), pages 193-209, August.
    5. C. A. Ferguson & A. W. Bowman & E. M. Scott & L. Carvalho, 2007. "Model comparison for a complex ecological system," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(3), pages 691-711, July.
    6. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, June.
    7. 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.
    8. Fuentes, Montserrat, 2007. "Approximate Likelihood for Large Irregularly Spaced Spatial Data," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 321-331, March.
    9. Bowman, A. W. & Azzalini, A., 2003. "Computational aspects of nonparametric smoothing with illustrations from the sm library," Computational Statistics & Data Analysis, Elsevier, vol. 42(4), pages 545-560, April.
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    Cited by:

    1. Sotirios Bersimis & Stavros Degiannakis & Dimitrios Georgakellos, 2017. "Real-time monitoring of carbon monoxide using value-at-risk measure and control charting," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(1), pages 89-108, January.
    2. Gromenko, Oleksandr & Kokoszka, Piotr, 2013. "Nonparametric inference in small data sets of spatially indexed curves with application to ionospheric trend determination," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 82-94.
    3. Mark Livingston & Francesca Pannullo & Adrian W. Bowman & E. Marian Scott & Nick Bailey, 2021. "Exploiting new forms of data to study the private rented sector: Strengths and limitations of a database of rental listings," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 663-682, April.
    4. David O'Donnell & Alastair Rushworth & Adrian W. Bowman & E. Marian Scott & Mark Hallard, 2014. "Flexible regression models over river networks," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(1), pages 47-63, January.
    5. Alastair M. Rushworth & Adrian W. Bowman & Mark J. Brewer & Simon J. Langan, 2013. "Distributed Lag Models for Hydrological Data," Biometrics, The International Biometric Society, vol. 69(2), pages 537-544, June.

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