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dc.contributor.authorBernard, Jordy
dc.date.accessioned2020-03-30T20:08:25Z
dc.date.available2020-03-30T20:08:25Z
dc.date.issued2019-05
dc.identifier.urihttp://hdl.handle.net/11122/10945
dc.descriptionMaster's Project (M.S.) University of Alaska Fairbanks, 2019en_US
dc.description.abstractKriging is a geostatistical interpolation method that produces predictions and prediction intervals. Classical kriging models use Euclidean (straight line) distance when modeling spatial autocorrelation. However, for estuaries, inlets, and bays, shortest-in-water distance may capture the system’s proximity dependencies better than Euclidean distance when boundary constraints are present. Shortest-in-water distance has been used to krige such regions (Little et al., 1997; Rathbun, 1998); however, the variance-covariance matrices used in these models have not been shown to be mathematically valid. In this project, a new kriging model is developed for irregularly shaped regions in R 2 . This model incorporates the notion of flow connected distance into a valid variance-covariance matrix through the use of a random walk on a lattice, process convolutions, and the non-stationary kriging equations. The model developed in this paper is compared to existing methods of spatial prediction over irregularly shaped regions using water quality data from Puget Sound.en_US
dc.language.isoen_USen_US
dc.titleA geostatistical model based on Brownian motion to Krige regions in R2 with irregular boundaries and holesen_US
dc.typeMaster's Projecten_US
dc.type.degreemsen_US
dc.identifier.departmentDepartment of Mathematics and Statisticsen_US
dc.contributor.chairMcIntyre, Julie
dc.contributor.chairBarry, Ron
dc.contributor.committeeGoddard, Scott
refterms.dateFOA2020-03-30T20:08:26Z


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