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dc.contributor.advisorGodtliebsen, Fred
dc.contributor.authorGeilhufe, Marc
dc.date.accessioned2013-07-04T12:10:23Z
dc.date.available2013-07-04T12:10:23Z
dc.date.issued2013-06-26
dc.description.abstractThe focus of this thesis is the analysis of both regular and irregular lattice data, described in three papers. Paper I presents an extension of the pyramid algorithm for efficient Maximal Overlap Discrete Wavelet Transform coefficient calculation. As opposed to common usage of two-dimensional wavelet decomposition, it here also allows for different scales in the vertical and horizontal direction. Wavelet variances are investigated for scale-dependent analysis of spatial patterns. A practical geoscience application with Synthetic Aperture Radar images of sea ice illustrates its potential. In Paper II wavelet variances are used to develop a novel test for isotropy of random fields. Isotropy is frequently assumed in spatial modeling. It requires the covariance or variogram function of a stationary or intrinsically stationary spatial process to depend only on the distance between two locations, independent of direction. In a simulation study, the presented test for isotropy is applied to realizations of various isotropic and anisotropic Gaussian random fields. Its performance is superior compared to existing methods, both on the general applicability and the consistent rejection rate close to the nominal level for isotropic fields, while the power for anisotropic fields is comparable to or better than other methods. An example connected to the manufacturing of paper also demonstrates practical applicability of the wavelet-based isotropy test. In Paper III microbiology test results of influenza A disease counts in North Norway are modeled to study the pattern of disease spread. The focus is to link the disease incidence to movement networks, with human travel patterns described by actual traffic data or a power law approach. Models with fixed, seasonal and random effects are used and extended with various covariates. An additional analysis is performed by grouping the data into adults and non-adults. All models are compared by one-step-ahead predictions using proper scoring rules.en
dc.description.doctoraltypeph.d.en
dc.description.popularabstractArbeidet er presentert i form av tre artikler. I hver artikkel utvikles det metoder for å løse et bestemt problem, og hver av metodene demonstreres på en praktisk anvendelse. Det første arbeidet bruker såkalte wavelet-varianser for å trekke ut informasjon fra satellittbilder som avbilder havis nord for Alaska. Her kobles fysikalske endringer i isen til statistiske endringer i satellittbildene i løpet av et år. Den andre artikkelen beskriver en ny test for å undersøke om en prosess er isotropisk, noe som innen fagfeltet statistikk innebærer at prosessens kovariansfunksjon kun avhenger av avstanden mellom to punkter i rommet uavhengig av retningen. Testens anvendelighet illustreres i et eksempel forbundet med papirproduksjon. I den tredje artikkelen modelleres utbredelse av influensa A-virus i Troms og Finnmark. Her fokuseres det på hvordan man kan analysere sykdomstilfeller ut fra reisemønster mellom kommuner. I artikkelen beskrives det flere modeller som også involverer ulike påvirkninger som for eksempel innbyggertall.en
dc.description.sponsorshipUniversity of Tromsøen
dc.descriptionThe papers of this thesis are not available in Munin: <br/>1. Geilhufe, M., Percival, D. B. and Stern, H. L.: 'Two-dimensional wavelet variance estimation with application to sea ice SAR images', Computers & Geosciences (2013), vol. 54:351-360, available at <a href=http://dx.doi.org/10.1016/j.cageo.2012.11.020>http://dx.doi.org/10.1016/j.cageo.2012.11.020</a> <br/>2. Thon K., Geilhufe, M. and Percival, D. B.: 'A multiscale wavelet-based test for isotropy of random fields on a regular lattice' (manuscript) <br/>3. Geilhufe, M., Held, L., Skrøvseth, S. O., Simonsen, G. S. and Godtliebsen, F.: 'Power law approximations of movement network data for modeling infectious disease spread' (manuscript)en
dc.identifier.isbn978-82-8236-098-2
dc.identifier.isbn978-82-8236-099-9
dc.identifier.urihttps://hdl.handle.net/10037/5247
dc.identifier.urnURN:NBN:no-uit_munin_4959
dc.language.isoengen
dc.publisherUniversitetet i Tromsøen
dc.publisherUniversity of Tromsøen
dc.rights.accessRightsopenAccess
dc.rights.holderCopyright 2013 The Author(s)
dc.subject.courseIDDOKTOR-004en
dc.subjectVDP::Mathematics and natural science: 400::Mathematics: 410::Statistics: 412en
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Matematikk: 410::Statistikk: 412en
dc.titleStatistical Analysis of Lattice Data with Wavelet Variance Methods and Spatiotemporal Modeling of Infectious Disease Spreaden
dc.typeDoctoral thesisen
dc.typeDoktorgradsavhandlingen


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