Functional Data Analysis on Spectroscopic Data

Simple item page
dc.contributor.advisorCox, Dennis D.
dc.contributor.committeeMemberScott, David W
dc.contributor.committeeMemberZhang, Yin
dc.creatorWang, Lu
dc.date.accessioned2016-02-05T15:20:16Z
dc.date.available2016-02-05T15:20:16Z
dc.date.created2015-12
dc.date.issued2016-01-28
dc.date.submittedDecember 2015
dc.date.updated2016-02-05T15:20:16Z
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.
dc.format.mimetypeapplication/pdf
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>.
dc.identifier.urihttps://hdl.handle.net/1911/88382
dc.language.isoeng
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.
dc.subjectFunctional Data
dc.subjectLog additive model
dc.subjectLocal polynomial regression
dc.titleFunctional Data Analysis on Spectroscopic Data
dc.typeThesis
dc.type.materialText
thesis.degree.departmentStatistics
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
WANG-DOCUMENT-2015.pdf
Size:
27.14 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
PROQUEST_LICENSE.txt
Size:
5.84 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
LICENSE.txt
Size:
2.6 KB
Format:
Plain Text
Description:
Download
Collections
Rice University Electronic Theses and Dissertations