Three applications of linear dimension reduction.
| dc.contributor.advisor | Young, Dean M. | |
| dc.creator | Odom, Gabriel Jairus. 1988- | |
| dc.date.accessioned | 2018-01-25T14:13:03Z | |
| dc.date.available | 2018-01-25T14:13:03Z | |
| dc.date.created | 2017-12 | |
| dc.date.issued | 2017-08-23 | |
| dc.date.submitted | December 2017 | |
| dc.date.updated | 2018-01-25T14:13:03Z | |
| dc.description.abstract | Linear 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.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/2104/10182 | |
| dc.language.iso | en | |
| dc.rights.accessrights | Worldwide access. | |
| dc.rights.accessrights | Access changed 5/21/20. | |
| dc.subject | Linear dimension reduction. | |
| dc.title | Three applications of linear dimension reduction. | |
| dc.type | Thesis | |
| dc.type.material | text | |
| local.embargo.lift | 2019-12-01 | |
| local.embargo.terms | 2019-12-01 | |
| thesis.degree.department | Baylor University. Dept. of Statistical Sciences. | |
| thesis.degree.grantor | Baylor University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Ph.D. |
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