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Asymptotics and Computation for Spatial Statistics

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Advances and Challenges in Space-time Modelling of Natural Events

Part of the book series: Lecture Notes in Statistics ((LNSP,volume 207))

Abstract

Asymptotics describe large sample properties of statistical inferences. Useful asymptotics are those that can help with the statistical inferences. For example, asymptotics can help identify statistically and computationally efficient estimators. The study of asymptotics in spatial statistics is complicated by the fact there are more than one asymptotic frameworks in spatial statistics and the asymptotic results are very different under the different asymptotic frameworks. This chapter reviews some results under these asymptotic frameworks and shows how the asymptotic results can help alleviate the computational challenges in the analysis of massive spatial data.

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Acknowledgements

This research is supported by the US National Science Foundation grants DMS-0833323 and IIS-1028291.

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Authors and Affiliations

  1. Department of Statistics, Purdue University, West Lafayette, USA

    Hao Zhang

Authors
  1. Hao Zhang
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Correspondence to Hao Zhang .

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Editors and Affiliations

  1. Facultad de Derecho y Ciencias Sociales, Área de Estadística, Universidad de Castilla la Mancha, Avenida Camilo Jose Cela 14, Ciudad Real, 13005, Spain

    Emilio Porcu

  2. , Statistics, Universidad de Castilla la Mancha, Cobertizo San Pedro Martir, Toledo, 45071, Spain

    José–María Montero

  3. , Mathematics Stochastics, Universität Göttingen, Goldschmidtstrasse 7, Göttingen, 37077, Germany

    Martin Schlather

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Zhang, H. (2012). Asymptotics and Computation for Spatial Statistics. In: Porcu, E., Montero, J., Schlather, M. (eds) Advances and Challenges in Space-time Modelling of Natural Events. Lecture Notes in Statistics(), vol 207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17086-7_10

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