Semivariogram estimation: asymptotic theory and applications

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Title Semivariogram estimation: asymptotic theory and applications
Publication Type dissertation
School or College College of Science
Department Mathematics
Author Kerby, Brent
Date 2016
Description The semivariogram is a function characterizing the second-order dependence structure of an intrinsically stationary random field; its estimation has applications in spatial statistics, particularly in the construction of optimal predictors of the random field at unobserved locations. In this work, we establish conditions under which the empirical isotropic semivariogram converges to the semivariogram uniformly on compact sets. In preparation for these results, we also establish sufficient conditions for stationary Gaussian random fields to be -mixing, in terms of the spectral density. We also introduce two new applications of semivariogram estimation: a method for digital image compression, and a refinement of the Moran's I test for spatial autocorrelation.
Type Text
Publisher University of Utah
Subject Gaussian random fields; image compression; mixing conditions; Moran's I test; semivariogram; spatial statistics
Dissertation Name Doctor of Philosophy
Language eng
Rights Management ©Brent Kerby
Format application/pdf
Format Medium application/pdf
Format Extent 2,502,400 bytes
Identifier etd3/id/4223
ARK ark:/87278/s6r81pkc
Setname ir_etd
ID 197768
Reference URL https://collections.lib.utah.edu/ark:/87278/s6r81pkc
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