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.
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References
Acosta J, Osorio F, Vallejos R (2016) Effective sample size for line transect sampling models with an application to marine macroalgae. Journal of Agricultural, Biological, and Environmental Statistics. Forthcoming.
Angulo JM, Madrid AE, Mateu J (2016) Point pattern analysis of spatial deformation and blurring effects on exceedances. Journal of Agricultural, Biological, and Environmental Statistics. Forthcoming.
Bevilacqua M, Alegria A, Velandia D, Porcu E (2016) Composite likelihood inference for multivariate Gaussian random fields. Journal of Agricultural, Biological, and Environmental Statistics. Forthcoming.
Chilés JP, Delfiner P (1999) Geostatistics: modeling spatial uncertainty, Wiley.
Christakos G (1992) Random Fields Models in Earth Sciences, Academic Press.
Christakos G, Hristopoulos D (1998) Spatio-temporal environmental health modelling: a tractatus stochasticus, Springer.
Cobb L, Watson B (1980) Statistical catastrophe theory: An overview. Mathematical Modeling, 1: 311-317.
Cressie, N (1993) Statistics for Spatial Data, Wiley.
Daley DJ, Vere Jones D (2002) An introduction to the theory of point processes, Springer.
Fassò A, Finazzi F, Ndongo F (2016) European population exposure to airborne pollutants based on a multivariate spatio-temporal model. Journal of Agricultural, Biological, and Environmental Statistics. Forthcoming.
Geller RJ, Jackson DD, Kagan Y, Mulargia F (1997). Earthquakes cannot be predicted. Science, 275, Issue 5306: 1616.
Huser R, Genton M (2016) Non-stationary dependence structures for spatial extremes. Journal of Agricultural, Biological, and Environmental Statistics. Forthcoming.
Lee M, Genton M, Jun M (2016) Testing self-similarity through Lamperti transformations. Journal of Agricultural, Biological, and Environmental Statistics. Forthcoming.
Møller J, Waagepetersen RP (2005) Statistical inference and simulation for spatial point processes, Chapman & Hall.
Woo G (1999) The mathematics of natural catastrophes. Imperial College Press.
Acknowledgments
J. Mateu has been supported by projects MTM2013-43917-P of the Spanish Ministry of Economy and Competitiveness, and by Grant P1-1B2015-40.
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Mateu, J., Porcu, E. Guest Editors’ Introduction to the Special Issue on “Seismomatics: Space–Time Analysis of Natural or Anthropogenic Catastrophes”. JABES 21, 403–406 (2016). https://doi.org/10.1007/s13253-016-0263-4
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DOI: https://doi.org/10.1007/s13253-016-0263-4