Tuning parameter selection for a penalized maximum likelihood estimator of species richness

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dc.contributor.advisorWillis, Amy D
dc.contributor.authorPaynter, Alexander Caldwell
dc.date.accessioned2019-08-14T22:29:52Z
dc.date.available2019-08-14T22:29:52Z
dc.date.issued2019-08-14
dc.date.submitted2019
dc.descriptionThesis (Master's)--University of Washington, 2019
dc.description.abstractOur goal is estimating the true number of classes in a population. We focus on the scenario where multiple frequency count tables have been collected from the same population. In this setting we demonstrate the efficacy of a previously published penalized maximum likelihood method. Four novel methods to tune the requisite penalization parameter are proposed. The performance of all proposed tuning methods is compared in simulations.
dc.embargo.termsOpen Access
dc.format.mimetypeapplication/pdf
dc.identifier.otherPaynter_washington_0250O_20282.pdf
dc.identifier.urihttp://hdl.handle.net/1773/44064
dc.language.isoen_US
dc.rightsCC BY-NC-SA
dc.subjectabundance
dc.subjectmicrobial ecology
dc.subjectpenalization
dc.subjectspecies richness
dc.subjectBiostatistics
dc.subject.otherBiostatistics
dc.titleTuning parameter selection for a penalized maximum likelihood estimator of species richness
dc.typeThesis

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