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dc.contributor.authorMoody, Marla Marie.
dc.creatorMoody, Marla Marie.en_US
dc.date.accessioned2011-10-31T18:02:51Z
dc.date.available2011-10-31T18:02:51Z
dc.date.issued1993en_US
dc.identifier.urihttp://hdl.handle.net/10150/186233
dc.description.abstractThe motivation for this dissertation is to develop innovations in spatial, environmental data analyses, using kriging and kernel estimation, that form a basis for an eventual automation of the calculations. Special consideration should be given to the different requirements for environmental data as compared to the mining data generally used in the evaluation of kriging applications. It is common to use standard search neighborhoods in the applications of kriging. It is one object of this dissertation to develop variable search neighborhoods and to extend the use of these search neighborhoods to experimental variogram calculations. Other objectives include incorporating one dimensional kernel estimation into variogram calculation; and augmenting kriging with two and three dimensional kernel estimators. These three different areas require the development of programs to accomplish the following: (1) Generate elliptical neighborhoods with variable parameters in two dimensions and ellipsoidal neighborhoods with variable parameters in three dimensions; and calculate experimental variograms using these neighborhoods to limit the number of data pairs used and thereby reduce the effects of drift. (2) Calculate experimental variograms with a one dimensional kernel to separate the bin width from the number of points which is not possible with the standard experimental variogram. (3) Use two or three dimensional kernel estimators to provide an alternate to kriging.
dc.language.isoenen_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.subjectDissertations, Academic.en_US
dc.subjectHydrology.en_US
dc.subjectGeology.en_US
dc.titleConstrained optimal neighborhoods and kernel estimators as improvements to applications of kriging.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.contributor.chairMyers, Donald E.en_US
dc.identifier.oclc716299558en_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.contributor.committeememberLamb, Georgeen_US
dc.contributor.committeememberWarrick, Arthuren_US
dc.identifier.proquest9322764en_US
thesis.degree.disciplineApplied Mathematicsen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.namePh.D.en_US
dc.description.noteThis item was digitized from a paper original and/or a microfilm copy. If you need higher-resolution images for any content in this item, please contact us at repository@u.library.arizona.edu.
dc.description.admin-noteOriginal file replaced with corrected file September 2023.
refterms.dateFOA2018-08-23T11:19:59Z
html.description.abstractThe motivation for this dissertation is to develop innovations in spatial, environmental data analyses, using kriging and kernel estimation, that form a basis for an eventual automation of the calculations. Special consideration should be given to the different requirements for environmental data as compared to the mining data generally used in the evaluation of kriging applications. It is common to use standard search neighborhoods in the applications of kriging. It is one object of this dissertation to develop variable search neighborhoods and to extend the use of these search neighborhoods to experimental variogram calculations. Other objectives include incorporating one dimensional kernel estimation into variogram calculation; and augmenting kriging with two and three dimensional kernel estimators. These three different areas require the development of programs to accomplish the following: (1) Generate elliptical neighborhoods with variable parameters in two dimensions and ellipsoidal neighborhoods with variable parameters in three dimensions; and calculate experimental variograms using these neighborhoods to limit the number of data pairs used and thereby reduce the effects of drift. (2) Calculate experimental variograms with a one dimensional kernel to separate the bin width from the number of points which is not possible with the standard experimental variogram. (3) Use two or three dimensional kernel estimators to provide an alternate to kriging.


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