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dc.contributor.advisorYoung, Dean M.
dc.creatorOdom, Gabriel Jairus. 1988-
dc.date.accessioned2018-01-25T14:13:03Z
dc.date.available2018-01-25T14:13:03Z
dc.date.created2017-12
dc.date.issued2017-08-23
dc.date.submittedDecember 2017
dc.identifier.urihttp://hdl.handle.net/2104/10182
dc.description.abstractLinear Dimension Reduction (LDR) has many uses in engineering, business, medicine, economics, data science and others. LDR can be employed when observations are recorded with many correlated features to reduce the number of features upon which statistical inference may be necessary. Some of the benefits of LDR are to increase the signal to noise ratio in noisy data, rotate features into orthogonal space to reduce feature correlation effects, reduce the number of parameters to estimate, and decrease computational and memory costs associated with model fitting. In this manuscript, we will discuss applications of LDR to poorly-posed classification, ill-posed classification, and statistical process monitoring.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectLinear dimension reduction.
dc.titleThree applications of linear dimension reduction.
dc.typeThesis
dc.rights.accessrightsWorldwide access.
dc.rights.accessrightsAccess changed 5/21/20.
dc.type.materialtext
thesis.degree.namePh.D.
thesis.degree.departmentBaylor University. Dept. of Statistical Sciences.
thesis.degree.grantorBaylor University
thesis.degree.levelDoctoral
dc.date.updated2018-01-25T14:13:03Z
local.embargo.lift2019-12-01
local.embargo.terms2019-12-01


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