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dc.contributor.advisorGilbert Strang.en_US
dc.contributor.authorGorsich, David John, 1968-en_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Mathematics.en_US
dc.date.accessioned2005-09-27T20:03:34Z
dc.date.available2005-09-27T20:03:34Z
dc.date.copyright2000en_US
dc.date.issued2000en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/9029
dc.descriptionThesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2000.en_US
dc.descriptionIncludes bibliographical references (p. 140-148).en_US
dc.description.abstractCrucial in spatial interpolation of stochastic processes is the determination of the underlying dependency of the data. The dependency can be represented by an underlying covariogram, variogram, or generalized covariogram. Estimating this function in a nonparametric way is the theme of this thesis. If the function can be found accurately, then kriging is the optimal linear interpolation technique. A nev,· technique for variogram model selection using the derivative of the empirical variogram and non-negative least squares is discussed. The eigenstructure of the spatial design matrix, the key matrix in Matheron's variogram estimator is determined. Then a nonparametric estimator of the variogram and covariogram of a spatial stochastic process is found. The optimal node selection is determined as well as conditions when the spectral coefficients can be found without a non-linear algorithm. A method of extending isotropic positive definite functions in ]Rd is determined in order to avoid a Gibbs effect on the Fourier-Bessel expansion. Finally, a nonparametric estimator of the generalized covariance is discussed.en_US
dc.description.statementofresponsibilityby David John Gorsich.en_US
dc.format.extent148 p.en_US
dc.format.extent10416786 bytes
dc.format.extent10416544 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582
dc.subjectMathematics.en_US
dc.titleNonparametric modeling of dependencies for spatial interpolationen_US
dc.typeThesisen_US
dc.description.degreePh.D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mathematics
dc.identifier.oclc47848724en_US


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