Symmetry, stability, and spatio-temporal dependence in multinomial Markov random field models

dc.contributor.advisor Kaiser, Mark S
dc.contributor.advisor Nordman, Daniel
dc.contributor.advisor Genschel, Ulrike
dc.contributor.advisor Roy, Vivekananda
dc.contributor.author McClernon, Kellie L
dc.contributor.department Statistics en_US
dc.date.accessioned 2023-01-10T17:14:47Z
dc.date.available 2023-01-10T17:14:47Z
dc.date.issued 2022-12
dc.date.updated 2023-01-10T17:14:47Z
dc.description.abstract Recent developments in Markov random field models for multinomials fail to treat the critical lack of symmetry among the categories when spatial dependence is incorporated. We demonstrate that the traditional full-rank parameterization exhibits lack of symmetry and recommend a new parameterization - the symmetric. We establish important model properties and regions of model stability for both Markov field models. Lastly, we extend the symmetric Markov random field model to include both temporal and spatial dependence through two distinct formulations to illustrate the difference among the resulting dependence structures.
dc.format.mimetype PDF
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/Dw88xbgw
dc.language.iso en
dc.language.rfc3066 en
dc.subject.disciplines Statistics en_US
dc.subject.keywords auto-models en_US
dc.subject.keywords categorical data en_US
dc.subject.keywords conditionally specified models en_US
dc.subject.keywords lattice data en_US
dc.title Symmetry, stability, and spatio-temporal dependence in multinomial Markov random field models
dc.type article en_US
dc.type.genre dissertation en_US
dspace.entity.type Publication
thesis.degree.discipline Statistics en_US
thesis.degree.grantor Iowa State University en_US
thesis.degree.level dissertation $
thesis.degree.name Doctor of Philosophy en_US
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