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Guest Editors’ Introduction to the Special Issue on “Seismomatics: Space–Time Analysis of Natural or Anthropogenic Catastrophes”

Author

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  • J. Mateu

    (Universitat Jaume I)

  • E. Porcu

    (University Federico Santa Maria)

Abstract

Seismomatics is the fusion of mathematics, statistics, physics and data mining at the service of those scientific disciplines interested in the space–time analysis of natural or anthropogenic catastrophes. This special issue on seismomatics has been motivated by a conference of the same name, which took place in Valparaiso (Chile) from 5th to 9th of January 2015. The selection of papers comprises both new methodological proposals and a wide range of applications related to natural or anthropogenic catastrophes. We highlight statistical analysis of marine macroalgae, of annual minimum water levels of the Nile River, of massive data on chlorophyll, of temperature maxima recorded over a complex topography, and of airborne pollutants in relation to the spatial spread of human population across Europe.

Suggested Citation

  • J. Mateu & E. Porcu, 2016. "Guest Editors’ Introduction to the Special Issue on “Seismomatics: Space–Time Analysis of Natural or Anthropogenic Catastrophes”," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 403-406, September.
    • Handle: RePEc:spr:jagbes:v:21:y:2016:i:3:d:10.1007_s13253-016-0263-4
    DOI: 10.1007/s13253-016-0263-4
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    References listed on IDEAS

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    1. Raphaël Huser & Marc G. Genton, 2016. "Non-Stationary Dependence Structures for Spatial Extremes," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 470-491, September.
    2. Moreno Bevilacqua & Alfredo Alegria & Daira Velandia & Emilio Porcu, 2016. "Composite Likelihood Inference for Multivariate Gaussian Random Fields," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 448-469, September.
    3. Myoungji Lee & Marc G. Genton & Mikyoung Jun, 2016. "Testing Self-Similarity Through Lamperti Transformations," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 426-447, September.
    4. Jonathan Acosta & Felipe Osorio & Ronny Vallejos, 2016. "Effective Sample Size for Line Transect Sampling Models with an Application to Marine Macroalgae," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 407-425, September.
    5. A. E. Madrid & J. M. Angulo & J. Mateu, 2016. "Point Pattern Analysis of Spatial Deformation and Blurring Effects on Exceedances," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 512-530, September.
    6. Alessandro Fassò & Francesco Finazzi & Ferdinand Ndongo, 2016. "European Population Exposure to Airborne Pollutants Based on a Multivariate Spatio-Temporal Model," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 492-511, September.
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