Functional Data Analysis on Spectroscopic Data

dc.contributor.advisorCox, Dennis D.en_US
dc.contributor.committeeMemberScott, David Wen_US
dc.contributor.committeeMemberZhang, Yinen_US
dc.creatorWang, Luen_US
dc.date.accessioned2016-02-05T15:20:16Zen_US
dc.date.available2016-02-05T15:20:16Zen_US
dc.date.created2015-12en_US
dc.date.issued2016-01-28en_US
dc.date.submittedDecember 2015en_US
dc.date.updated2016-02-05T15:20:16Zen_US
dc.description.abstractCervical cancer is a very common type of cancer that is highly curable if treated early. We are investigating spectroscopic devices that make in-vivo cervical tissue measurements to detect pre-cancerous and cancerous lesions. This dissertation is focused on new methods and algorithms to improve the performance of the device, treating the spectroscopic measurements as functional data. The first project is to calibrate the device measurements using correction factors from a log additive model, based on results from a carefully designed experiment. The second project is a peak finding algorithm using local polynomial regression to get accurate peak location and height estimates of one of the standards (Rhodamine B) measurements from the experiment. We propose a plug-in bandwidth selection method to estimate curve peak location and height. Simulation results and asymptotic properties are presented. The third project is based on patient measurements, particularly when the diseased and non-diseased cases are highly unbalanced. A marginalized corruption methodology is introduced to improve the classification results. Performance of several classification methods is compared.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationWang, Lu. "Functional Data Analysis on Spectroscopic Data." (2016) Diss., Rice University. <a href="https://hdl.handle.net/1911/88382">https://hdl.handle.net/1911/88382</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/88382en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectFunctional Dataen_US
dc.subjectLog additive modelen_US
dc.subjectLocal polynomial regressionen_US
dc.titleFunctional Data Analysis on Spectroscopic Dataen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentStatisticsen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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