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Rapid numerical approximation method for integrated covariance functions over irregular data regions
Author(s) -
Simonson Peter,
Nychka Douglas,
Bandyopadhyay Soutir
Publication year - 2020
Publication title -
stat
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.61
H-Index - 18
ISSN - 2049-1573
DOI - 10.1002/sta4.275
Subject(s) - covariance , covariance function , fourier transform , block (permutation group theory) , algorithm , process (computing) , variable (mathematics) , spatial analysis , mathematics , computer science , mathematical analysis , geometry , statistics , operating system
In many practical applications, spatial data are often collected at areal levels (i.e., block data), and the inferences and predictions about the variable at points or blocks different from those at which it has been observed typically depend on integrals of the underlying continuous spatial process. In this paper, we describe a method based on Fourier transforms by which multiple integrals of covariance functions over irregular data regions may be numerically approximated with the same level of accuracy as traditional methods, but at a greatly reduced computational expense.