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.
Keywords
- Spectral Density
- Asymptotic Distribution
- Asymptotic Result
- Predictive Distribution
- Spatial Data Analysis
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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This research is supported by the US National Science Foundation grants DMS-0833323 and IIS-1028291.
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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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DOI: https://doi.org/10.1007/978-3-642-17086-7_10
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